GridCARE is a leading venture-backed startup solving the most critical constraint in AI’s growth trajectory: immediate access to power. As demand for computing skyrockets, access to energy has become the defining bottleneck in the AI infrastructure race. While leading tech companies invest billions in speculative, long-term solutions that may take decades to arrive, GridCARE’s pioneering physics-based generative AI platform unlocks gigawatts of hidden capacity in today’s electric grid — enabling hyperscalers, data center developers, and utilities to power AI infrastructure years sooner than conventional approaches and without costly upgrades.
Founded at Stanford’s Doerr School of Sustainability and backed by leading investors, GridCARE has assembled a world-class team spanning power systems, AI, and infrastructure.
At GridCARE, you will:
Work at the intersection of AI, energy, and infrastructure — the foundation of the next industrial revolution.
Partner with hyperscalers, developers, and utilities on high-impact, real-world deployments.
Help shape a more abundant, efficient, and resilient energy future for the digital era.
Join a company defining a new category — capacity acceleration for AI.
Receive competitive compensation, equity, and benefits in a fast-growth, mission-driven environment.
We're looking for an engineer in the early stages of their career who has demonstrated the ability to solve difficult technical problems in a professional or research setting to work on hard computational problems in the electric grid. This role is ideal for someone who enjoys solving difficult optimization problems and building software that has a direct impact on critical infrastructure. You’ll work closely with experienced engineers and researchers to build tools that help make the grid more flexible, efficient, and resilient as data centers, renewables, storage, and new regulations reshape how electricity systems operate.
Build optimization and simulation software for large-scale electric grid planning and operations problems.
Develop models and algorithms for resource scheduling, network constraints, system reliability, and large-scale operational planning.
Translate mathematical ideas into clear, tested, maintainable code.
Analyze computational performance, identify bottlenecks, and improve reliability.
Learn, customize, and fine-tune optimization algorithms.
Work with real-world data and constraints from large physical systems.
Collaborate across software engineering, applied AI, and power systems.
Take ownership of important modeling and optimization challenges.
A Master's degree or equivalent experience in a quantitative field such as engineering, computer science, applied math, physics, or operations research.
Strong fundamentals in math, algorithms, and software engineering.
Experience writing production-quality code in a language such as Python or Julia.
Experience with optimization solvers, simulation tools, and large-scale numerical workflows.
Ability to reason from first principles and learn unfamiliar technical areas quickly.
Strong debugging, communication, and collaboration skills.
Effectively leverage modern AI tools and workflows to accelerate model development, experimentation, and software engineering.
1–5 years of industry experience, or equivalent applied research experience, in optimization, modeling, simulation, or scientific software development.
Exposure to power systems, energy markets, or the utility industry.
Exposure to optimization, operations research, numerical methods, scientific computing, or large-scale infrastructure systems.
Interest in electricity infrastructure, climate, energy, or industrial AI.
Competitive salary, performance bonus, and equity.
Comprehensive health, dental, and vision coverage.
Lunch provided three days a week in office.
Hybrid schedule: 3 days in office for collaboration, 2 days remote for focused work.
Access to leading academic, industry, and government partners in the AI-energy ecosystem.
A mission-driven team focused on shaping the future of the energy transition.
$140,000–$220,000
Join us in tackling one of the most important infrastructure challenges of our time - enabling the energy foundation for the age of AI.
","descriptionPlain":"ABOUT US\n\nGridCARE is a leading venture-backed startup solving the most critical constraint in AI’s growth trajectory: immediate access to power. As demand for computing skyrockets, access to energy has become the defining bottleneck in the AI infrastructure race. While leading tech companies invest billions in speculative, long-term solutions that may take decades to arrive, GridCARE’s pioneering physics-based generative AI platform unlocks gigawatts of hidden capacity in today’s electric grid — enabling hyperscalers, data center developers, and utilities to power AI infrastructure years sooner than conventional approaches and without costly upgrades.\n\nFounded at Stanford’s Doerr School of Sustainability and backed by leading investors, GridCARE has assembled a world-class team spanning power systems, AI, and infrastructure.\n\nAt GridCARE, you will:\n\n - Work at the intersection of AI, energy, and infrastructure — the foundation of the next industrial revolution.\n\n - Partner with hyperscalers, developers, and utilities on high-impact, real-world deployments.\n\n - Help shape a more abundant, efficient, and resilient energy future for the digital era.\n\n - Join a company defining a new category — capacity acceleration for AI.\n\n - Receive competitive compensation, equity, and benefits in a fast-growth, mission-driven environment.\n\n\n\n\nABOUT THE ROLE\n\nWe're looking for an engineer in the early stages of their career who has demonstrated the ability to solve difficult technical problems in a professional or research setting to work on hard computational problems in the electric grid. This role is ideal for someone who enjoys solving difficult optimization problems and building software that has a direct impact on critical infrastructure. You’ll work closely with experienced engineers and researchers to build tools that help make the grid more flexible, efficient, and resilient as data centers, renewables, storage, and new regulations reshape how electricity systems operate.\n\n\n\n\nWHAT YOU’LL DO\n\n - Build optimization and simulation software for large-scale electric grid planning and operations problems.\n\n - Develop models and algorithms for resource scheduling, network constraints, system reliability, and large-scale operational planning.\n\n - Translate mathematical ideas into clear, tested, maintainable code.\n\n - Analyze computational performance, identify bottlenecks, and improve reliability.\n\n - Learn, customize, and fine-tune optimization algorithms.\n\n - Work with real-world data and constraints from large physical systems.\n\n - Collaborate across software engineering, applied AI, and power systems.\n\n - Take ownership of important modeling and optimization challenges.\n\n\nWHAT WE’RE LOOKING FOR\n\n - A Master's degree or equivalent experience in a quantitative field such as engineering, computer science, applied math, physics, or operations research.\n\n - Strong fundamentals in math, algorithms, and software engineering.\n\n - Experience writing production-quality code in a language such as Python or Julia.\n\n - Experience with optimization solvers, simulation tools, and large-scale numerical workflows.\n\n - Ability to reason from first principles and learn unfamiliar technical areas quickly.\n\n - Strong debugging, communication, and collaboration skills.\n\n - Effectively leverage modern AI tools and workflows to accelerate model development, experimentation, and software engineering.\n\n - 1–5 years of industry experience, or equivalent applied research experience, in optimization, modeling, simulation, or scientific software development.\n\n\nNICE TO HAVE\n\n - Exposure to power systems, energy markets, or the utility industry.\n\n - Exposure to optimization, operations research, numerical methods, scientific computing, or large-scale infrastructure systems.\n\n - Interest in electricity infrastructure, climate, energy, or industrial AI.\n\n\n\n\n\nWHAT WE OFFER\n\n - Competitive salary, performance bonus, and equity.\n\n - Comprehensive health, dental, and vision coverage.\n\n - Lunch provided three days a week in office.\n\n - Hybrid schedule: 3 days in office for collaboration, 2 days remote for focused work.\n\n - Access to leading academic, industry, and government partners in the AI-energy ecosystem.\n\n - A mission-driven team focused on shaping the future of the energy transition.\n\n\n\n\nSALARY RANGE\n\n$140,000–$220,000\n\nJoin us in tackling one of the most important infrastructure challenges of our time - enabling the energy foundation for the age of AI.","compensation":{"compensationTierSummary":null,"scrapeableCompensationSalarySummary":null,"compensationTiers":[],"summaryComponents":[]}},{"id":"e05539f3-b7a7-462f-9cc1-dd2ccc37805b","title":"Physical Systems Modeling Engineer","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"Redwood City","shouldDisplayCompensationOnJobPostings":false,"secondaryLocations":[],"publishedAt":"2026-08-20T22:23:09.302+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"Redwood City"}},"jobUrl":"https://jobs.ashbyhq.com/gridcare/e05539f3-b7a7-462f-9cc1-dd2ccc37805b","applyUrl":"https://jobs.ashbyhq.com/gridcare/e05539f3-b7a7-462f-9cc1-dd2ccc37805b/application","descriptionHtml":"Location: Redwood City, CA (Hybrid) or Remote – US
Team: Engineering
Type: Full-time
GridCARE is a leading venture-backed startup solving the most critical constraint in AI’s growth trajectory: immediate access to power. As demand for computing skyrockets, access to energy has become the defining bottleneck in the AI infrastructure race. While leading tech companies invest billions in speculative, long-term solutions that may take decades to arrive, GridCARE’s pioneering physics-based generative AI platform unlocks gigawatts of hidden capacity in today’s electric grid — enabling hyperscalers, data center developers, and utilities to power AI infrastructure years sooner than conventional approaches and without costly upgrades.
Founded at Stanford’s Doerr School of Sustainability and backed by leading climate-tech and deep-tech investors, GridCARE has assembled a world-class team spanning power systems, AI, and infrastructure.
At GridCARE, you will:
Work at the intersection of AI, energy, and infrastructure — the foundation of the next industrial revolution.
Partner with hyperscalers, developers, and utilities on high-impact, real-world deployments.
Help shape a more abundant, efficient, and resilient energy future for the digital era.
Join a company defining a new category — capacity acceleration for AI.
Receive competitive compensation, equity, and benefits in a fast-growth, mission-driven environment.
We are looking for a quantitative engineer to help build the models at the core of how GridCARE understands data center power: how facilities consume energy today, and how that consumption will evolve over time. In this role, you will develop simulation and forecasting models for existing and future data centers and the components inside data centers that will be used to support planning decisions across the full horizon, from intraday operations to multi-year infrastructure development. The goal is not simply to predict future demand, but to build models that can generate realistic future operating scenarios under a wide range of assumptions and conditions.
This role is well suited for someone who enjoys reasoning about complex systems, working with imperfect data, and translating real-world behavior into quantitative models. You should be excited to learn new domains and apply rigorous analytical thinking to difficult problems.
Build models that accurately represent the physical systems inside of a data center
Develop simulation frameworks that capture uncertainty, growth trajectories, and operational behavior across planning horizons.
Explore how changes in technology, utilization, deployment strategies, and external factors influence future energy consumption.
Analyze historical data to identify patterns, drivers, and sources of uncertainty.
Evaluate model performance and continuously improve assumptions, methodologies, and predictive accuracy.
Collaborate with engineers, researchers, and business stakeholders to translate practical questions into quantitative analyses.
Communicate model assumptions, limitations, and findings clearly to both technical and non-technical audiences.
Take ownership of key modeling components and drive them from concept through validation and deployment.
Bachelor's, Master's, or PhD in a quantitative field, with 1–5+ years of relevant industry or applied research experience.
Experience working with data, mathematical models, simulations, forecasting problems, and time-series analysis.
Experience with forecasting under uncertainty, probabilistic modeling, or scenario generation.
A passion for understanding how data centers work and operate: their components, load behavior, operating constraints, and how that operation interacts with the electric grid.
Proficiency in Python or a similar programming language.
Ability to think from first principles and learn unfamiliar technical domains quickly.
Comfort working with ambiguity and open-ended questions.
Strong written and verbal communication skills.
Demonstrated ability to effectively use AI tools in technical workflows.
Exposure to infrastructure systems, energy systems, cloud computing, data centers, telecommunications networks, or other large-scale engineered systems.
Exposure to data center, GPU, CPU power consumption modeling.
Experience modeling data center to grid interaction, such as interconnection, load response, or capacity constraints.
Experience developing models used to support real-world operational or planning decisions.
Experience working with large datasets.
Work on a problem that directly affects the speed of deploying AI and energy infrastructure
Own technical direction on a product built from the ground up alongside domain experts
Meaningful organizational influence in a small, high-trust team
Competitive salary, equity, and benefits; hybrid in Redwood City with remote flexibility
We believe small teams of strong engineers can solve important, real-world infrastructure problems. We value clear thinking, strong ownership, pragmatic execution, and systems that hold up in production. If you're excited about technical leadership at the intersection of energy, infrastructure, and modern SaaS — we'd love to talk.
$140,000 – $250,000
","descriptionPlain":"Location: Redwood City, CA (Hybrid) or Remote – US\n\nTeam: Engineering\n\nType: Full-time\n\n\nABOUT GRIDCARE\n\nGridCARE is a leading venture-backed startup solving the most critical constraint in AI’s growth trajectory: immediate access to power. As demand for computing skyrockets, access to energy has become the defining bottleneck in the AI infrastructure race. While leading tech companies invest billions in speculative, long-term solutions that may take decades to arrive, GridCARE’s pioneering physics-based generative AI platform unlocks gigawatts of hidden capacity in today’s electric grid — enabling hyperscalers, data center developers, and utilities to power AI infrastructure years sooner than conventional approaches and without costly upgrades.\n\nFounded at Stanford’s Doerr School of Sustainability and backed by leading climate-tech and deep-tech investors, GridCARE has assembled a world-class team spanning power systems, AI, and infrastructure.\n\n\n\nAt GridCARE, you will:\n\n - Work at the intersection of AI, energy, and infrastructure — the foundation of the next industrial revolution.\n\n - Partner with hyperscalers, developers, and utilities on high-impact, real-world deployments.\n\n - Help shape a more abundant, efficient, and resilient energy future for the digital era.\n\n - Join a company defining a new category — capacity acceleration for AI.\n\n - Receive competitive compensation, equity, and benefits in a fast-growth, mission-driven environment.\n\n\nABOUT THE ROLE\n\nWe are looking for a quantitative engineer to help build the models at the core of how GridCARE understands data center power: how facilities consume energy today, and how that consumption will evolve over time. In this role, you will develop simulation and forecasting models for existing and future data centers and the components inside data centers that will be used to support planning decisions across the full horizon, from intraday operations to multi-year infrastructure development. The goal is not simply to predict future demand, but to build models that can generate realistic future operating scenarios under a wide range of assumptions and conditions.\n\nThis role is well suited for someone who enjoys reasoning about complex systems, working with imperfect data, and translating real-world behavior into quantitative models. You should be excited to learn new domains and apply rigorous analytical thinking to difficult problems.\n\n\nWHAT YOU'LL DO\n\n - Build models that accurately represent the physical systems inside of a data center\n\n - Develop simulation frameworks that capture uncertainty, growth trajectories, and operational behavior across planning horizons.\n\n - Explore how changes in technology, utilization, deployment strategies, and external factors influence future energy consumption.\n\n - Analyze historical data to identify patterns, drivers, and sources of uncertainty.\n\n - Evaluate model performance and continuously improve assumptions, methodologies, and predictive accuracy.\n\n - Collaborate with engineers, researchers, and business stakeholders to translate practical questions into quantitative analyses.\n\n - Communicate model assumptions, limitations, and findings clearly to both technical and non-technical audiences.\n\n - Take ownership of key modeling components and drive them from concept through validation and deployment.\n\n\nWHAT WE'RE LOOKING FOR\n\n - Bachelor's, Master's, or PhD in a quantitative field, with 1–5+ years of relevant industry or applied research experience. \n\n - Experience working with data, mathematical models, simulations, forecasting problems, and time-series analysis.\n\n - Experience with forecasting under uncertainty, probabilistic modeling, or scenario generation.\n\n - A passion for understanding how data centers work and operate: their components, load behavior, operating constraints, and how that operation interacts with the electric grid.\n\n - Proficiency in Python or a similar programming language.\n\n - Ability to think from first principles and learn unfamiliar technical domains quickly.\n\n - Comfort working with ambiguity and open-ended questions.\n\n - Strong written and verbal communication skills.\n\n - Demonstrated ability to effectively use AI tools in technical workflows.\n\n\nNICE TO HAVE\n\n - Exposure to infrastructure systems, energy systems, cloud computing, data centers, telecommunications networks, or other large-scale engineered systems.\n\n - Exposure to data center, GPU, CPU power consumption modeling.\n\n - Experience modeling data center to grid interaction, such as interconnection, load response, or capacity constraints.\n\n - Experience developing models used to support real-world operational or planning decisions.\n\n - Experience working with large datasets.\n\n\nWHY GRIDCARE\n\n - Work on a problem that directly affects the speed of deploying AI and energy infrastructure\n\n - Own technical direction on a product built from the ground up alongside domain experts\n\n - Meaningful organizational influence in a small, high-trust team\n\n - Competitive salary, equity, and benefits; hybrid in Redwood City with remote flexibility\n\n\nOUR PHILOSOPHY\n\nWe believe small teams of strong engineers can solve important, real-world infrastructure problems. We value clear thinking, strong ownership, pragmatic execution, and systems that hold up in production. If you're excited about technical leadership at the intersection of energy, infrastructure, and modern SaaS — we'd love to talk.\n\n\nSALARY RANGE\n\n$140,000 – $250,000","compensation":{"compensationTierSummary":null,"scrapeableCompensationSalarySummary":null,"compensationTiers":[],"summaryComponents":[]}},{"id":"7aff5c11-5cda-4f40-89c7-0a239431b1bd","title":"Senior Site Reliability Engineer ","department":"Engineering","team":"Engineering","employmentType":"FullTime","location":"Redwood City","shouldDisplayCompensationOnJobPostings":false,"secondaryLocations":[],"publishedAt":"2026-09-14T22:18:53.402+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"Redwood City"}},"jobUrl":"https://jobs.ashbyhq.com/gridcare/7aff5c11-5cda-4f40-89c7-0a239431b1bd","applyUrl":"https://jobs.ashbyhq.com/gridcare/7aff5c11-5cda-4f40-89c7-0a239431b1bd/application","descriptionHtml":"GridCARE is a leading venture-backed startup solving the most critical constraint in AI’s growth trajectory: immediate access to power. As demand for computing skyrockets, access to energy has become the defining bottleneck in the AI infrastructure race. While leading tech companies invest billions in speculative, long-term solutions that may take decades to arrive, GridCARE’s pioneering physics-based generative AI platform unlocks gigawatts of hidden capacity in today’s electric grid — enabling hyperscalers, data center developers, and utilities to power AI infrastructure years sooner than conventional approaches and without costly upgrades.
Founded at Stanford’s Doerr School of Sustainability and backed by leading climate-tech and deep-tech investors, GridCARE has assembled a world-class team spanning power systems, AI, and infrastructure.
At GridCARE, you will:
⚡ Work at the intersection of AI, energy, and infrastructure — the foundation of the next industrial revolution.
🤝 Partner with hyperscalers, developers, and utilities on high-impact, real-world deployments.
🌎 Help shape a more abundant, efficient, and resilient energy future for the digital era.
🚀 Join a company defining a new category — capacity acceleration for AI.
💰 Receive competitive compensation, equity, and benefits in a fast-growth, mission-driven environment.
Learn more about GridCARE:
TechCrunch: GridCARE thinks more than 100 GW of data-center capacity is hiding in the grid
Data Center Dynamics: From Years to Months — Creating an AI Fast Lane for Data Centers
GridCARE Raises $64 Million Series A to Create a New Category: Power Acceleration
We're looking for a Senior SRE to own the reliability, scalability, and observability of our production systems. You'll work closely with platform and data engineering to keep high-throughput, data-intensive services running at the availability our customers (utilities, data center operators) require.
Design and operate infrastructure on AWS using Terraform and Kubernetes
Build monitoring, alerting, and observability (Prometheus, Grafana, Datadog, or similar) with meaningful SLOs/SLIs
Automate away toil — deployment pipelines, capacity management, self-healing systems
Partner with engineering on architecture reviews to catch reliability and scalability risks before they ship
Manage database and data pipeline reliability for large-scale, real-time grid data processing
Drive security and compliance best practices across infrastructure
Required
5+ years in SRE, DevOps, or infrastructure engineering roles
Deep experience with Kubernetes, Terraform/IaC, and cloud platforms (AWS Preferred)
Strong scripting/programming ability (Python, Bash)
Observability Experience (Prometheus, Grafana, Datadog)
Track record of running on-call for production systems and leading incident response
Experience with CI/CD pipelines (Github Actions) and infrastructure automation
Experience with Gitops concepts and tooling (ArgoCD/Flux)
Solid understanding of networking, distributed systems, and database reliability
Comfortable operating in a fast-moving startup environment with ambiguity
Preferred
Experience with data-intensive or real-time processing systems
Background in energy, climate tech, or critical infrastructure
Experience scaling infrastructure through hypergrowth
On-Prem Kubernetes Deployment Experience
Windows Server Administration Experience
Competitive salary, performance bonus, and equity.
Comprehensive health, dental, and vision coverage.
Lunch provided three days a week in office.
Hybrid schedule for local employees: 3 days in office for collaboration, 2 days remote for focused work.
Access to leading academic, industry, and government partners in the AI-energy ecosystem.
A mission-driven team focused on shaping the future of the energy transition.
$180,000-$230,000 Total
Join us in tackling one of the most important infrastructure challenges of our time — enabling the energy foundation for the age of AI.
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]\n\n\n\n\nABOUT US\n\nGridCARE is a leading venture-backed startup solving the most critical constraint in AI’s growth trajectory: immediate access to power. As demand for computing skyrockets, access to energy has become the defining bottleneck in the AI infrastructure race. While leading tech companies invest billions in speculative, long-term solutions that may take decades to arrive, GridCARE’s pioneering physics-based generative AI platform unlocks gigawatts of hidden capacity in today’s electric grid — enabling hyperscalers, data center developers, and utilities to power AI infrastructure years sooner than conventional approaches and without costly upgrades.\n\nFounded at Stanford’s Doerr School of Sustainability and backed by leading climate-tech and deep-tech investors, GridCARE has assembled a world-class team spanning power systems, AI, and infrastructure.\n\n\n\nAt GridCARE, you will:\n\n⚡ Work at the intersection of AI, energy, and infrastructure — the foundation of the next industrial revolution.\n\n🤝 Partner with hyperscalers, developers, and utilities on high-impact, real-world deployments.\n\n🌎 Help shape a more abundant, efficient, and resilient energy future for the digital era.\n\n🚀 Join a company defining a new category — capacity acceleration for AI.\n\n💰 Receive competitive compensation, equity, and benefits in a fast-growth, mission-driven environment.\n\n\n\nLearn more about GridCARE:\n\n - TechCrunch: GridCARE thinks more than 100 GW of data-center capacity is hiding in the grid https://techcrunch.com/2025/05/27/gridcare-thinks-more-than-100-gw-of-data-center-capacity-is-hiding-in-the-grid/\n\n - Utility Dive: Portland General Electric invests in AI-powered flexibility to speed data-center connection https://www.utilitydive.com/news/gridcare-portland-general-pge-data-centers/803097/\n\n - Data Center Dynamics: From Years to Months — Creating an AI Fast Lane for Data Centers https://www.datacenterdynamics.com/en/analysis/from-years-to-months-creating-an-ai-fast-lane-for-grid-connection/\n\n - GridCARE Raises $64 Million Series A to Create a New Category: Power Acceleration https://www.businesswire.com/news/home/20260514546216/en/GridCARE-Raises-%2464-Million-Series-A-from-Leading-AI-and-Energy-Investors-to-Create-a-New-Category-Power-Acceleration-for-AI\n\n\n\n\n\n\nJOB DESCRIPTION\n\nWe're looking for a Senior SRE to own the reliability, scalability, and observability of our production systems. You'll work closely with platform and data engineering to keep high-throughput, data-intensive services running at the availability our customers (utilities, data center operators) require.\n\n\n\n\nRESPONSIBILITIES\n\n - Design and operate infrastructure on AWS using Terraform and Kubernetes\n\n - Build monitoring, alerting, and observability (Prometheus, Grafana, Datadog, or similar) with meaningful SLOs/SLIs\n\n - Automate away toil — deployment pipelines, capacity management, self-healing systems\n\n - Partner with engineering on architecture reviews to catch reliability and scalability risks before they ship\n\n - Manage database and data pipeline reliability for large-scale, real-time grid data processing\n\n - Drive security and compliance best practices across infrastructure\n\n\n\n\nQUALIFICATIONS\n\nRequired\n\n - 5+ years in SRE, DevOps, or infrastructure engineering roles\n\n - Deep experience with Kubernetes, Terraform/IaC, and cloud platforms (AWS Preferred)\n\n - Strong scripting/programming ability (Python, Bash)\n\n - Observability Experience (Prometheus, Grafana, Datadog)\n\n - Track record of running on-call for production systems and leading incident response\n\n - Experience with CI/CD pipelines (Github Actions) and infrastructure automation\n\n - Experience with Gitops concepts and tooling (ArgoCD/Flux)\n\n - Solid understanding of networking, distributed systems, and database reliability\n\n - Comfortable operating in a fast-moving startup environment with ambiguity\n\n\n\nPreferred\n\n - Experience with data-intensive or real-time processing systems\n\n - Background in energy, climate tech, or critical infrastructure\n\n - Experience scaling infrastructure through hypergrowth\n\n - On-Prem Kubernetes Deployment Experience\n\n - Windows Server Administration Experience\n\n\n\n\n\n\nWHAT WE OFFER\n\n - Competitive salary, performance bonus, and equity.\n\n - Comprehensive health, dental, and vision coverage.\n\n - Lunch provided three days a week in office.\n\n - Hybrid schedule for local employees: 3 days in office for collaboration, 2 days remote for focused work.\n\n - Access to leading academic, industry, and government partners in the AI-energy ecosystem.\n\n - A mission-driven team focused on shaping the future of the energy transition.\n\n\n\n\n\n\nSALARY RANGE\n\n$180,000-$230,000 Total \n\n\n\nJoin us in tackling one of the most important infrastructure challenges of our time — enabling the energy foundation for the age of AI.","compensation":{"compensationTierSummary":null,"scrapeableCompensationSalarySummary":null,"compensationTiers":[],"summaryComponents":[]}},{"id":"61c3a627-f5a8-4952-817f-19e5be993e95","title":"Principal Power Systems Engineer","department":"Engineering","team":"Power Systems","employmentType":"FullTime","location":"Remote, US","shouldDisplayCompensationOnJobPostings":false,"secondaryLocations":[],"publishedAt":"2026-09-17T14:49:34.466+00:00","isListed":true,"isRemote":true,"workplaceType":"Remote","address":{"postalAddress":{"addressCountry":"United States"}},"jobUrl":"https://jobs.ashbyhq.com/gridcare/61c3a627-f5a8-4952-817f-19e5be993e95","applyUrl":"https://jobs.ashbyhq.com/gridcare/61c3a627-f5a8-4952-817f-19e5be993e95/application","descriptionHtml":"We are looking for a Principal Power Systems Engineer with hands-on interconnection study experience and a passion for improving the grid integration process through AI and automation. In this role, you will lead in-depth technical analyses, build out our dynamics and short-circuit duty study capability, analyze results, and present study findings directly to utilities and customers. You will serve as a technical subject-matter expert for the engineering team, and help lead the plan that connects planning-side capacity assessment to how utilities implement it in operations. You will work with utilities, developers, and regulators to accelerate interconnection timelines. This is a unique opportunity to combine deep power systems expertise and stakeholder engagement, with cutting-edge AI solutions to modernize how studies are performed.
Perform and lead load and generation interconnection studies, including steady-state, short-circuit, or dynamic analysis with widely accepted commercial tools.
Build out GridCARE's approach to dynamics and short-circuit duty studies — defining the methodology and selecting cost-effective tooling so we can run these studies when they are required.
Develop power flow and stability models to evaluate system impact for new interconnections, including data centers and renewable generation (inverter-based resources).
Serve as a technical subject-matter expert for the engineering team, advising on study methodology and interconnection process questions.
Help lead the plan that bridges planning and operations — translating capacity assessment and qualification into an approach utilities can actually implement in the operations plane.
Collaborate directly with utilities, developers, and regulators to understand their needs and facilitate smooth interconnection processes and compliance with standards.
Present study results, recommendations, and process findings directly to utility planners, project managers, and customer stakeholders.
Evaluate interconnection options, system upgrades, and Grid-Enhancing Technologies (GETs), providing clear recommendations to stakeholders.
Partner with AI and automation teams to streamline study processes and improve accuracy.
Stay engaged with the latest interconnection news, standards, and emerging solutions in power systems, including the DOE i2X forum and ESIG modeling task force.
Required
Bachelor's degree in Electrical Engineering (Power Systems focus) or a related field. Master's or PhD preferred.
10+ years of experience in power system studies, transmission planning, or interconnection, at a utility or a consulting firm.
Demonstrated hands-on experience running interconnection studies specifically — a minimum of two years.
Proficiency with commercial power system tools (e.g., PSSE, PSLF, PowerWorld, PSCAD, PowerGEM TARA, ASPEN).
Strong coding skills in Python or equivalent, including use of power system tool APIs (psspy, simauto, pypowergem).
Strong understanding of FERC, NERC, and ISO/RTO interconnection processes.
Experience working with or for a utility transmission planning or interconnection team.
Knowledge of steady-state and dynamic load modeling requirements.
A creative, forward-looking approach to problem solving — someone who finds paths forward rather than stopping at the bottlenecks in current planning practice — with a bias toward action.
Excellent problem-solving, communication, and collaboration skills in a fast-paced startup environment, with a focus on client interaction.
Preferred
Interconnection process experience across multiple regions, particularly ERCOT/Texas, the Midwest, or the Southeast.
Working proficiency in Python or equivalent, including use of power system tool APIs (psspy, simauto, pypowergem).
Experience in applying machine-learning and AI applications to power system studies.
Able to leverage AI code development tools such as Cursor, Copilot and Claude Code to accelerate development.
Experience deploying power system applications in the cloud.
Familiarity with version control (git) and basic software engineering practices.
Ability to write well-documented, tested, and maintainable code.
Competitive salary and equity.
Comprehensive benefits package.
Hybrid work environment with catered lunches and snacks.
The chance to work at the intersection of AI, automation, and power systems alongside experienced engineers and leading researchers.
A mission-driven team focused on shaping the future of the energy transition.
Location
Redwood City preferred, open to remote
$250,000-$350,000 Total
Join us in tackling one of the most important infrastructure challenges of our time — enabling the energy foundation for the age of AI.
GridCARE accelerates power for AI. We help data centers connect to the grid years faster by identifying available capacity and operationalizing flexible interconnections, and we help utilities serve large loads without compromising reliability. Our Power Operations platform provides real-time monitoring and dispatch, drives long-term energy management and system optimization, and seamlessly connects utility planning, utility operations, and data center operations. It builds on decades of experience deploying best-in-class DERMS and virtual power plants at AutoGrid.
Learn more about GridCARE:
TechCrunch: GridCARE thinks more than 100 GW of data-center capacity is hiding in the grid
Data Center Dynamics: From Years to Months — Creating an AI Fast Lane for Data Centers
GridCARE Raises $64 Million Series A to Create a New Category: Power Acceleration
THE ROLE
Where flexible interconnections become real
Our Johnson City Operations Center is where flexible interconnections become real. As a Power Operations Desk Operator, you will staff a 24/7 desk that monitors data center loads, on-site generation, and grid conditions in real time, and you will execute the curtailment and dispatch actions that keep those sites within their interconnection agreements. You are the person utilities and site teams call when conditions change, and the person who makes sure nothing gets missed at 3 a.m. This role reports to the Director of 24/7 & Customer Support Operations.
We are hiring at multiple levels, from trainable early-career operators to seasoned control room professionals. Your title, responsibilities, and pay will match your experience.
RESPONSIBILITIES
What you'll do
Monitor real-time telemetry (load, generation, frequency, voltage, and grid signals) across a fleet of data center sites and flexible interconnection assets.
Execute dispatch and curtailment instructions from utilities and ISOs/RTOs within contractual response windows, and verify that sites respond as expected.
Follow, and help improve, operating procedures, checklists, and escalation paths; document every action in the shift log.
Coordinate over phone, radio, and chat with utility control centers, data center facility teams, and GridCARE engineering during normal and abnormal conditions.
Identify alarms, anomalies, and data quality issues early; troubleshoot with the field and software teams and escalate when needed.
Conduct thorough shift turnovers so the incoming operator has full situational awareness.
Participate in drills, post-event reviews, and continuous improvement of the operations desk.
Senior operators: mentor junior staff, lead complex events, and serve as a subject matter expert on utility interconnection and market operations.
QUALIFICATIONS
Required at all levels
Ability to work a fixed 12-hour day or fixed 12-hour night schedule, including weekends and holidays, on-site in Johnson City, TN.
Strong situational awareness, attention to detail, and the discipline to follow procedures exactly, especially under pressure.
Clear, professional communication over the phone and in writing.
Comfort working with software dashboards, alarm systems, and spreadsheets; ability to learn new tools quickly.
High school diploma or equivalent. Offers for this position are contingent on a background check and a pre-employment drug screen.
Operator I / II (0–5 years)
Candidates with a technical or military background, or an associate's degree in electrical, energy, or a related field, will stand out.
Interest in the power grid and a desire to earn industry certifications on the job.
Senior Operator (5+ years)
Experience in a utility control room, generation dispatch, NOC, or a similar 24/7 operations environment.
Working knowledge of SCADA/EMS systems, demand response, or DER/DERMS operations.
Familiarity with ISO/RTO market and reliability operations (PJM, MISO, TVA, ERCOT, or similar).
NERC System Operator certification is valued but not required.
SCHEDULE
Twelve-hour shifts on a 2-2-3 rotation
Rotation. Two on, two off, three on, then the reverse. Seven 12-hour shifts every two weeks, never more than three in a row, and every other weekend off as a three-day weekend.
Fixed assignment. You are hired into a fixed day shift (6:00 AM – 6:00 PM) or a fixed night shift (6:00 PM – 6:00 AM); days and nights do not rotate. Tell us your preference when you apply.
Hours. The desk is staffed 24/7/365, including weekends and holidays. Overtime is built into the schedule and paid at 1.5x, and night-shift operators receive a $3.00/hr differential.
COMPENSATION
Placement within a range depends on directly relevant experience and certifications. Overtime and night differential are paid on top.
Pay by level
OPERATOR I: 0–2 yrs · trainable, strong aptitude
$24 – $29 /hr
OPERATOR II: 2–5 yrs · dispatch, NOC, or SCADA
$29 – $36 /hr
SENIOR OPERATOR: 5+ yrs · NERC cert or utility control room
$38 – $48 /hr
BENEFITS
What else you get
Medical, dental, and vision coverage, and a 401(k) retirement plan.
Flexible paid time off, scheduled with your manager so the desk stays covered.
Twelve observed holidays plus two floating holidays. Because the desk never closes, operators receive holiday pay whether the holiday falls on a shift or a day off, with additional pay for holidays worked.
Equity in a fast-growing company at the center of the AI power buildout.
Paid training, with support for NERC certification and continuing education.
Join us in tackling one of the most important infrastructure challenges of our time — enabling the energy foundation for the age of AI.
GridCARE is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
","descriptionPlain":"GridCARE accelerates power for AI. We help data centers connect to the grid years faster by identifying available capacity and operationalizing flexible interconnections, and we help utilities serve large loads without compromising reliability. Our Power Operations platform provides real-time monitoring and dispatch, drives long-term energy management and system optimization, and seamlessly connects utility planning, utility operations, and data center operations. It builds on decades of experience deploying best-in-class DERMS and virtual power plants at AutoGrid.\n\nLearn more about GridCARE:\n\n - TechCrunch: GridCARE thinks more than 100 GW of data-center capacity is hiding in the grid https://techcrunch.com/2025/05/27/gridcare-thinks-more-than-100-gw-of-data-center-capacity-is-hiding-in-the-grid/\n\n - Utility Dive: Portland General Electric invests in AI-powered flexibility to speed data-center connection https://www.utilitydive.com/news/gridcare-portland-general-pge-data-centers/803097/\n\n - Data Center Dynamics: From Years to Months — Creating an AI Fast Lane for Data Centers https://www.datacenterdynamics.com/en/analysis/from-years-to-months-creating-an-ai-fast-lane-for-grid-connection/\n\n - GridCARE Raises $64 Million Series A to Create a New Category: Power Acceleration https://www.businesswire.com/news/home/20260514546216/en/GridCARE-Raises-%2464-Million-Series-A-from-Leading-AI-and-Energy-Investors-to-Create-a-New-Category-Power-Acceleration-for-AI\n\n\n\nTHE ROLE\n\nWhere flexible interconnections become real\n\nOur Johnson City Operations Center is where flexible interconnections become real. As a Power Operations Desk Operator, you will staff a 24/7 desk that monitors data center loads, on-site generation, and grid conditions in real time, and you will execute the curtailment and dispatch actions that keep those sites within their interconnection agreements. You are the person utilities and site teams call when conditions change, and the person who makes sure nothing gets missed at 3 a.m. This role reports to the Director of 24/7 & Customer Support Operations.\n\nWe are hiring at multiple levels, from trainable early-career operators to seasoned control room professionals. Your title, responsibilities, and pay will match your experience.\n\n\n\nRESPONSIBILITIES\n\nWhat you'll do\n\n - Monitor real-time telemetry (load, generation, frequency, voltage, and grid signals) across a fleet of data center sites and flexible interconnection assets.\n\n - Execute dispatch and curtailment instructions from utilities and ISOs/RTOs within contractual response windows, and verify that sites respond as expected.\n\n - Follow, and help improve, operating procedures, checklists, and escalation paths; document every action in the shift log.\n\n - Coordinate over phone, radio, and chat with utility control centers, data center facility teams, and GridCARE engineering during normal and abnormal conditions.\n\n - Identify alarms, anomalies, and data quality issues early; troubleshoot with the field and software teams and escalate when needed.\n\n - Conduct thorough shift turnovers so the incoming operator has full situational awareness.\n\n - Participate in drills, post-event reviews, and continuous improvement of the operations desk.\n\n - Senior operators: mentor junior staff, lead complex events, and serve as a subject matter expert on utility interconnection and market operations.\n\n\n\nQUALIFICATIONS\n\nRequired at all levels\n\n - Ability to work a fixed 12-hour day or fixed 12-hour night schedule, including weekends and holidays, on-site in Johnson City, TN.\n\n - Strong situational awareness, attention to detail, and the discipline to follow procedures exactly, especially under pressure.\n\n - Clear, professional communication over the phone and in writing.\n\n - Comfort working with software dashboards, alarm systems, and spreadsheets; ability to learn new tools quickly.\n\n - High school diploma or equivalent. Offers for this position are contingent on a background check and a pre-employment drug screen.\n\nOperator I / II (0–5 years)\n\n - Candidates with a technical or military background, or an associate's degree in electrical, energy, or a related field, will stand out.\n\n - Interest in the power grid and a desire to earn industry certifications on the job.\n\nSenior Operator (5+ years)\n\n - Experience in a utility control room, generation dispatch, NOC, or a similar 24/7 operations environment.\n\n - Working knowledge of SCADA/EMS systems, demand response, or DER/DERMS operations.\n\n - Familiarity with ISO/RTO market and reliability operations (PJM, MISO, TVA, ERCOT, or similar).\n\n - NERC System Operator certification is valued but not required.\n\n\n\nSCHEDULE\n\nTwelve-hour shifts on a 2-2-3 rotation\n\n - Rotation. Two on, two off, three on, then the reverse. Seven 12-hour shifts every two weeks, never more than three in a row, and every other weekend off as a three-day weekend.\n\n - Fixed assignment. You are hired into a fixed day shift (6:00 AM – 6:00 PM) or a fixed night shift (6:00 PM – 6:00 AM); days and nights do not rotate. Tell us your preference when you apply.\n\n - Hours. The desk is staffed 24/7/365, including weekends and holidays. Overtime is built into the schedule and paid at 1.5x, and night-shift operators receive a $3.00/hr differential.\n\n\n\nCOMPENSATION \n\nPlacement within a range depends on directly relevant experience and certifications. Overtime and night differential are paid on top.\n\nPay by level\n\nOPERATOR I: 0–2 yrs · trainable, strong aptitude\n\n - $24 – $29 /hr\n\nOPERATOR II: 2–5 yrs · dispatch, NOC, or SCADA\n\n - $29 – $36 /hr\n\nSENIOR OPERATOR: 5+ yrs · NERC cert or utility control room\n\n - $38 – $48 /hr\n\n\n\n\n\nBENEFITS\n\nWhat else you get\n\n - Medical, dental, and vision coverage, and a 401(k) retirement plan.\n\n - Flexible paid time off, scheduled with your manager so the desk stays covered.\n\n - Twelve observed holidays plus two floating holidays. Because the desk never closes, operators receive holiday pay whether the holiday falls on a shift or a day off, with additional pay for holidays worked.\n\n - Equity in a fast-growing company at the center of the AI power buildout.\n\n - Paid training, with support for NERC certification and continuing education.\n\n\n\nJoin us in tackling one of the most important infrastructure challenges of our time — enabling the energy foundation for the age of AI.\n\n\n\nGridCARE is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.","compensation":{"compensationTierSummary":"$24 – $48 per hour • Multiple Ranges","scrapeableCompensationSalarySummary":null,"compensationTiers":[{"id":"058f5554-ac3e-4ece-9ef4-4c7e26e297d7","tierSummary":"$24 – $29 per hour","title":"Operator I: 0–2 yrs · trainable, strong aptitude","additionalInformation":null,"components":[{"id":"95ca3cf9-856f-48da-b542-120ec3f727dd","summary":"$24 – $29 per hour","compensationType":"Salary","interval":"1 HOUR","currencyCode":"USD","minValue":24,"maxValue":29}]},{"id":"a64dc781-0cdd-4128-9443-e0dfbf1af95c","tierSummary":"$29 – $36 per hour","title":"Operator II: 2-5 yrs · dispatch, NOC, or SCADA","additionalInformation":null,"components":[{"id":"9421bb6b-d6c5-4a4a-b467-1eda4c4af4a8","summary":"$29 – $36 per hour","compensationType":"Salary","interval":"1 HOUR","currencyCode":"USD","minValue":29,"maxValue":36}]},{"id":"cf6dc365-2e1d-494d-9ed1-7cf9f738b332","tierSummary":"$38 – $48 per hour","title":"Senior Operator: 5+ yrs · NERC cert or utility control room","additionalInformation":null,"components":[{"id":"9a8fec91-b30a-44d8-8b38-bb67029b0174","summary":"$38 – $48 per hour","compensationType":"Salary","interval":"1 HOUR","currencyCode":"USD","minValue":38,"maxValue":48}]}],"summaryComponents":[{"minValue":24,"maxValue":48,"currencyCode":"USD","interval":"1 HOUR","compensationType":"Salary"}]}},{"id":"4fefddae-4c5c-4f0e-8022-cdb56d844014","title":"Senior Platform Engineer","department":"Engineering","team":"Platform","employmentType":"FullTime","location":"Redwood City","shouldDisplayCompensationOnJobPostings":false,"secondaryLocations":[],"publishedAt":"2026-10-02T17:21:01.935+00:00","isListed":true,"isRemote":true,"workplaceType":"Hybrid","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"Redwood City"}},"jobUrl":"https://jobs.ashbyhq.com/gridcare/4fefddae-4c5c-4f0e-8022-cdb56d844014","applyUrl":"https://jobs.ashbyhq.com/gridcare/4fefddae-4c5c-4f0e-8022-cdb56d844014/application","descriptionHtml":"Senior Software Engineer, Platform
Location: Redwood City, CA (3 days/week in office)
Job Type: Full-time · Hybrid
GridCARE is the pioneer of Power Acceleration — a new category solving the most critical constraint in AI's growth trajectory: immediate access to power. As demand for computing skyrockets, access to energy has become the defining bottleneck in the AI infrastructure race. While leading tech companies invest billions in speculative, long-term solutions that may take decades to arrive, GridCARE's Energize™ platform uses physics-based AI to identify and activate gigawatts of hidden capacity in today's electric grid — enabling hyperscalers, data center developers, and utilities to power AI infrastructure years sooner than conventional approaches and without costly upgrades.
Founded at Stanford's Doerr School of Sustainability, GridCARE has assembled a world-class team spanning power systems, AI, and infrastructure, and is backed by leading climate-tech and deep-tech investors.
At GridCARE, you will:
⚡ Work at the intersection of AI, energy, and infrastructure — the foundation of the next industrial revolution.
🤝 Partner with hyperscalers, developers, and utilities on high-impact, real-world deployments.
🌎 Help shape a more abundant, efficient, and resilient energy future for the digital era.
🚀 Join a company defining a new category — Power Acceleration for AI.
💰 Receive competitive compensation, equity, and benefits in a fast-growth, mission-driven environment.
Learn more about GridCARE:
TechCrunch: GridCARE thinks more than 100 GW of data-center capacity is hiding in the grid
GridCARE Raises $64 Million Series A to Create a New Category: Power Acceleration
GridCARE Named a World Economic Forum Technology Pioneer for AI Power Innovation
We’re looking for a Senior Software Engineer, Platform to build the shared software foundations that let GridCARE turn grid intelligence into products: secure APIs, tenant-aware authorization, integrations with job orchestration systems, and reusable application services.
Working with our tech lead, product engineers, data owner, and power systems team, you’ll take these capabilities from architecture through production adoption—making them easy for developers and AI agents to discover, integrate, and operate. You’ll independently resolve implementation choices, build on managed services, and own rollout and production behavior, with the tech lead guiding platform architecture. You’ll raise the engineering bar through design reviews, code review, and mentoring.
Build the API platform. Use managed gateway and identity services to support browser applications and machine clients. Own the integration code, trusted identity and tenant context, request validation, and clear, versioned API contracts.
Partner on job orchestration. Work with data and power systems engineers to integrate job workflows with shared platform services. Build the API and access-control interfaces that connect products to these workflows, preserving tenant and study context through job submission, status, and result access.
Make authorization and tenant isolation dependable. Implement shared access controls across APIs, services, jobs, and data interfaces. Enforce resource ownership and study boundaries, including for internal users authorized to work with multiple customers, and define how permission changes affect ongoing work.
Make the platform easy to build on. Create reusable APIs, libraries, application templates, machine-readable contracts, and executable examples. Help developers and AI agents discover capabilities, compose workflows, and recover from errors.
Make systems dependable in production. Instrument services, investigate failures and performance bottlenecks, and partner with SRE on deployment, observability, and recovery.
Drive delivery and adoption. Turn ambiguous needs into incremental releases, make pragmatic build-versus-buy decisions, and help existing products adopt shared authentication, authorization, and execution patterns. Evolve interfaces safely and work with data and power systems engineers on integration boundaries.
Build reliable usage metering. Capture durable API and job events, attribute usage to the right tenant and principal, and handle retries and deduplication so usage records remain accurate.
Required
5+ years of relevant software engineering experience, with a track record of building shared backend capabilities for a multi-tenant product and owning their rollout and operation in production.
Strong Python engineering skills and experience building production APIs and services with clear interfaces, tests, and maintainable data models.
Hands-on experience building shared APIs, workflow services, or identity and access capabilities on managed services, with ownership of integration code, policies, and production behavior.
Strong distributed systems fundamentals: you can reason about concurrency, partial failures, retries, idempotency, consistency, and backpressure.
Practical experience implementing multi-tenant authorization: resource ownership, fine-grained permissions, trusted context across service boundaries, and access controls that hold through asynchronous execution.
Experience with relational databases, queues, cloud services, and diagnosing production behavior through logs, metrics, and traces.
Sound technical judgment and the ability to independently turn an unclear requirement into a reliable system adopted by other engineers. You can work with a tech lead on architecture and coordinate delivery across product, data, and domain teams.
Clear communication, thoughtful code review, and an interest in mentoring teammates. You can explain tradeoffs and work constructively across teams.
Required
Experience with FastAPI, Pydantic, OpenAPI, PostgreSQL, or the wider Python service ecosystem.
Experience with durable workflow or job orchestration systems such as Temporal, Prefect, or Airflow.
Experience with OAuth2/OIDC, Auth0 or similar identity providers, service identities, and relationship-based authorization systems such as OpenFGA.
Familiarity with AWS, Kubernetes, object storage, and OpenTelemetry; experience with durable event processing or usage metering.
Experience supporting computationally intensive, geospatial, time-series, or AI workloads.
Effective use of AI coding tools, with disciplined review and testing of generated code.
Competitive salary, performance bonus, and equity.
Comprehensive health, dental, and vision coverage.
Lunch provided three days a week in office.
Hybrid schedule: 3 days in office for collaboration, 2 days remote for focused work.
Access to leading academic, industry, and government partners in the AI-energy ecosystem.
A mission-driven team focused on shaping the future of the energy transition.
This is a hybrid, in-office role based in Redwood City, CA. Employees work onsite three days/week, Tu-Thurs.
$170,000 - $195,000 Total
Join us in tackling one of the most important infrastructure challenges of our time — enabling the energy foundation for the age of AI.
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]\n\n\n\nSenior Software Engineer, Platform\n\nLocation: Redwood City, CA (3 days/week in office)\n\nJob Type: Full-time · Hybrid\n\n\n\n\nABOUT US\n\nGridCARE is the pioneer of Power Acceleration — a new category solving the most critical constraint in AI's growth trajectory: immediate access to power. As demand for computing skyrockets, access to energy has become the defining bottleneck in the AI infrastructure race. While leading tech companies invest billions in speculative, long-term solutions that may take decades to arrive, GridCARE's Energize™ platform uses physics-based AI to identify and activate gigawatts of hidden capacity in today's electric grid — enabling hyperscalers, data center developers, and utilities to power AI infrastructure years sooner than conventional approaches and without costly upgrades.\n\nFounded at Stanford's Doerr School of Sustainability, GridCARE has assembled a world-class team spanning power systems, AI, and infrastructure, and is backed by leading climate-tech and deep-tech investors.\n\n\n\nAt GridCARE, you will:\n\n⚡ Work at the intersection of AI, energy, and infrastructure — the foundation of the next industrial revolution.\n\n🤝 Partner with hyperscalers, developers, and utilities on high-impact, real-world deployments.\n\n🌎 Help shape a more abundant, efficient, and resilient energy future for the digital era.\n\n🚀 Join a company defining a new category — Power Acceleration for AI.\n\n💰 Receive competitive compensation, equity, and benefits in a fast-growth, mission-driven environment.\n\nLearn more about GridCARE:\n\n - TechCrunch: GridCARE thinks more than 100 GW of data-center capacity is hiding in the grid https://techcrunch.com/2025/05/27/gridcare-thinks-more-than-100-gw-of-data-center-capacity-is-hiding-in-the-grid/\n\n - Utility Dive: Portland General Electric invests in AI-powered flexibility to speed data-center connection https://www.utilitydive.com/news/gridcare-portland-general-pge-data-centers/803097/\n\n - GridCARE Raises $64 Million Series A to Create a New Category: Power Acceleration https://www.businesswire.com/news/home/20260514546216/en/GridCARE-Raises-%2464-Million-Series-A-from-Leading-AI-and-Energy-Investors-to-Create-a-New-Category-Power-Acceleration-for-AI\n\n - GridCARE Named a World Economic Forum Technology Pioneer for AI Power Innovation https://www.businesswire.com/news/home/20260610932035/en/GridCARE-Named-a-World-Economic-Forum-Technology-Pioneer-for-AI-Power-Innovation\n\n\n\n\nTHE ROLE\n\nWe’re looking for a Senior Software Engineer, Platform to build the shared software foundations that let GridCARE turn grid intelligence into products: secure APIs, tenant-aware authorization, integrations with job orchestration systems, and reusable application services.\n\nWorking with our tech lead, product engineers, data owner, and power systems team, you’ll take these capabilities from architecture through production adoption—making them easy for developers and AI agents to discover, integrate, and operate. You’ll independently resolve implementation choices, build on managed services, and own rollout and production behavior, with the tech lead guiding platform architecture. You’ll raise the engineering bar through design reviews, code review, and mentoring.\n\n\n\n\nRESPONSIBILITIES\n\n - Build the API platform. Use managed gateway and identity services to support browser applications and machine clients. Own the integration code, trusted identity and tenant context, request validation, and clear, versioned API contracts.\n\n - Partner on job orchestration. Work with data and power systems engineers to integrate job workflows with shared platform services. Build the API and access-control interfaces that connect products to these workflows, preserving tenant and study context through job submission, status, and result access.\n\n - Make authorization and tenant isolation dependable. Implement shared access controls across APIs, services, jobs, and data interfaces. Enforce resource ownership and study boundaries, including for internal users authorized to work with multiple customers, and define how permission changes affect ongoing work.\n\n - Make the platform easy to build on. Create reusable APIs, libraries, application templates, machine-readable contracts, and executable examples. Help developers and AI agents discover capabilities, compose workflows, and recover from errors.\n\n - Make systems dependable in production. Instrument services, investigate failures and performance bottlenecks, and partner with SRE on deployment, observability, and recovery.\n\n - Drive delivery and adoption. Turn ambiguous needs into incremental releases, make pragmatic build-versus-buy decisions, and help existing products adopt shared authentication, authorization, and execution patterns. Evolve interfaces safely and work with data and power systems engineers on integration boundaries.\n\n - Build reliable usage metering. Capture durable API and job events, attribute usage to the right tenant and principal, and handle retries and deduplication so usage records remain accurate.\n\n\n\n\nQUALIFICATIONS\n\nRequired\n\n - 5+ years of relevant software engineering experience, with a track record of building shared backend capabilities for a multi-tenant product and owning their rollout and operation in production.\n\n - Strong Python engineering skills and experience building production APIs and services with clear interfaces, tests, and maintainable data models.\n\n - Hands-on experience building shared APIs, workflow services, or identity and access capabilities on managed services, with ownership of integration code, policies, and production behavior.\n\n - Strong distributed systems fundamentals: you can reason about concurrency, partial failures, retries, idempotency, consistency, and backpressure.\n\n - Practical experience implementing multi-tenant authorization: resource ownership, fine-grained permissions, trusted context across service boundaries, and access controls that hold through asynchronous execution.\n\n - Experience with relational databases, queues, cloud services, and diagnosing production behavior through logs, metrics, and traces.\n\n - Sound technical judgment and the ability to independently turn an unclear requirement into a reliable system adopted by other engineers. You can work with a tech lead on architecture and coordinate delivery across product, data, and domain teams.\n\n - Clear communication, thoughtful code review, and an interest in mentoring teammates. You can explain tradeoffs and work constructively across teams.\n\n\n\nRequired\n\n - Experience with FastAPI, Pydantic, OpenAPI, PostgreSQL, or the wider Python service ecosystem.\n\n - Experience with durable workflow or job orchestration systems such as Temporal, Prefect, or Airflow.\n\n - Experience with OAuth2/OIDC, Auth0 or similar identity providers, service identities, and relationship-based authorization systems such as OpenFGA.\n\n - Familiarity with AWS, Kubernetes, object storage, and OpenTelemetry; experience with durable event processing or usage metering.\n\n - Experience supporting computationally intensive, geospatial, time-series, or AI workloads.\n\n - Effective use of AI coding tools, with disciplined review and testing of generated code.\n\n\n\n\nWHAT WE OFFER\n\n - Competitive salary, performance bonus, and equity.\n\n - Comprehensive health, dental, and vision coverage.\n\n - Lunch provided three days a week in office.\n\n - Hybrid schedule: 3 days in office for collaboration, 2 days remote for focused work.\n\n - Access to leading academic, industry, and government partners in the AI-energy ecosystem.\n\n - A mission-driven team focused on shaping the future of the energy transition.\n\n\n\n\nLOCATION\n\n - This is a hybrid, in-office role based in Redwood City, CA. Employees work onsite three days/week, Tu-Thurs.\n\n\n\n\nSALARY RANGE\n\n$170,000 - $195,000 Total\n\n\n\nJoin us in tackling one of the most important infrastructure challenges of our time — enabling the energy foundation for the age of AI.","compensation":{"compensationTierSummary":null,"scrapeableCompensationSalarySummary":null,"compensationTiers":[],"summaryComponents":[]}}],"apiVersion":"1"}