About Axiom
Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.
What you will do
You will help build the computational and biological foundation for Axiom’s toxicity prediction platform.
Own the exploration and analysis of massive multimodal toxicity datasets spanning high-content imaging, transcriptomics, proteomics, ADME, mass spec, and functional cellular readouts.
Identify subtle biological signals that distinguish safe compounds from toxic compounds across human-relevant systems such as liver, heart, kidney, and immune biology.
Turn noisy, high-dimensional experimental data into clear biological insights, robust features, quality metrics, and model-ready datasets.
Analyze high-content imaging and transcriptomic data from primary human hepatocytes and multicellular hepatic systems, including phenotypes related to mitochondrial dysfunction, cholestasis, lipid accumulation, lysosomal stress, ER stress, cytotoxicity, and cellular morphology.
Conduct detailed model error analyses to understand what biology our models capture, where they fail, and what new data or assays are needed to improve them.
Collaborate with ML researchers to improve models that predict human toxicity as a function of dose, exposure, Cmax, in vitro potency, chemical structure, and biological response.
Develop computational approaches for extracting meaningful signal from imaging, transcriptomic, proteomic, and biochemical assays.
Design and improve quality control systems for large-scale, high-throughput biological datasets.
Work closely with wet lab scientists to shape new assays optimized not just for biological plausibility, but for predictive modeling.
Partner with leading pharma and biotech teams to interpret molecule toxicity profiles and help them understand the biology driving model predictions.
Help invent the future of computational toxicology: AI systems that do not just classify compounds, but explain mechanisms, reason over evidence, and guide better drug design.
What we are looking for
We are looking for someone who is unusually strong at both biology and computation.
You are a biologist who taught yourself to code because existing tools were not good enough for the questions you wanted to answer.
You are a computational scientist who loves being close to the raw experimental data, not just abstracted datasets.
You are exceptional at finding signal in messy biological data.
You have strong taste for what matters scientifically and can distinguish real biological insight from noise, artifacts, and overfit correlations.
You are excited by high-content imaging, transcriptomics, assay development, and the possibility of building the world’s largest experimental-to-clinical datasets.
You are obsessive about data quality, reproducibility, and scientific rigor.
You can bridge wet lab protocols and machine learning models, understanding how experimental design, assay biology, feature extraction, and modeling choices all interact.
You are excited to work across many biological systems and mechanisms, including hepatotoxicity, mitochondrial toxicity, cholestasis, reactive metabolites, transporter biology, immune-mediated toxicity, kidney toxicity, cardiac toxicity, and more.
You want to do science that matters in the real world, not just publish papers.
You have the ambition to help build a generational company from the ground up.
Technical skills we value
We do not expect every candidate to have all of these, but we are especially excited by experience with:
Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks
Statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation
High-content imaging analysis, microscopy, morphology profiling, and image-based phenotyping
CellProfiler, Cellpose, napari, OpenCV, scikit-image, or related image analysis tools
Transcriptomics, proteomics, mass spectrometry, ADME, or other high-dimensional biological datasets
High-throughput screening, assay development, automation, and experimental QC
Biological interpretation of model outputs and error modes
Scientific storytelling: turning complex analyses into clear, credible, inspiring narratives
About Axiom
Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.redict human drug outcomes dramatically better than animals and phase 1 clinical trials.
What you will do
You will sit at the center of Axiom’s chemistry, biology, modeling, and customer work.
Lead the analysis of model outputs across chemical series, targets, modalities, mechanisms, and clinical toxicity endpoints.
Identify where Axiom’s models perform well, where they fail, and what those failures reveal about chemistry, biology, exposure, or missing data.
Work with ML researchers to improve models that predict human toxicity as a function of chemical structure, in vitro potency, biological response, dose, Cmax, ADME, and clinical context.
Analyze large-scale chemistry datasets across thousands to hundreds of thousands of compounds for model training, evaluation, benchmarking, and dataset design.
Clean, curate, and structure chemical data, including compound identifiers, structures, salts, stereochemistry, dose/exposure information, ADME properties, targets, annotations, and clinical outcomes.
Use medicinal chemistry intuition to interpret model predictions, understand structure–toxicity relationships, and identify chemically meaningful patterns.
Partner directly with top drug hunters at leading pharma and biotech companies to interpret model outputs and help them make better program decisions.
Help design new experimental and molecular datasets based on model failures, customer needs, chemical space gaps, and real-world drug discovery use cases.
Work with Axiom’s mechanistic agent to connect chemical structure, biological readouts, phenotypic similarity, clinical outcomes, and proposed mechanisms of toxicity.
Influence active drug programs by helping teams understand whether toxicity risk is driven by exposure, potency, off-target biology, reactive metabolites, transporters, mitochondrial liability, cholestasis, immune mechanisms, or other drivers.
Shape Axiom’s product by translating customer feedback into better model outputs, visualizations, analyses, and workflows for medicinal chemists and toxicologists.
Help define how the best drug hunters in the world will use AI to design safer medicines.
What we are looking for
We are looking for someone who can combine medicinal chemistry judgment with computational depth.
You have an advanced degree in chemistry, computational chemistry, cheminformatics, medicinal chemistry, chemical biology, or equivalent experience inside a drug discovery organization.
You might identify as a computational chemist, cheminformatics scientist, ML for chemistry researcher, medicinal chemist with strong computational skills, or drug discovery scientist who became deeply technical.
You understand how real drug programs move from hit discovery to lead optimization to candidate selection.
You can reason about potency, selectivity, physicochemical properties, ADME, PK, exposure, safety margins, and clinical translatability.
You are excited by the challenge of connecting chemical structure to human outcomes.
You understand the limitations of current preclinical safety models and have strong opinions about how they should be improved.
You are comfortable analyzing large chemical datasets and drawing conclusions from a combination of data science, chemistry, and biological reasoning.
You can work directly with pharma customers, earn the trust of senior drug hunters, and communicate technical insights clearly.
You want to build tools that are not just scientifically interesting, but actually used to make decisions in real drug discovery programs.
Technical skills we value
We do not expect every candidate to have all of these, but we are especially excited by experience with:
Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks
RDKit, Datamol, DeepChem, or related cheminformatics tooling
Chemical structure processing, standardization, salt stripping, stereochemistry handling, scaffold analysis, similarity search, clustering, and molecular fingerprints
Large-scale chemical dataset curation and quality control
QSAR, molecular property prediction, ADME modeling, exposure modeling, or toxicity prediction
Dose-response modeling, curve fitting, calibration, benchmarking, uncertainty analysis, and model error analysis
SQL, cloud data workflows, and large-scale data processing
Drug discovery datasets involving targets, assays, potency, selectivity, ADME, PK, toxicology, or clinical outcomes
Scientific presentation and storytelling for medicinal chemists, toxicologists, and drug discovery leadership
About Axiom:
Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.
What you will do:
You will own major parts of Axiom’s computational mass spectrometry stack.
Analyze large-scale biological mass spectrometry datasets, primarily LC-MS/MS, across metabolomics, lipidomics, proteomics, and reactive metabolite workflows.
Build, improve, and scale computational pipelines for untargeted LC-MS/MS analysis using tools such as MZmine, OpenMS, MS-DIAL, GNPS, Skyline, or custom internal software.
Develop workflows for peak detection, alignment, normalization, annotation, batch correction, QC, feature filtering, compound identification, and downstream biological interpretation.
Turn raw mass spec data into model-ready representations that can be used by machine learning systems and mechanistic reasoning agents.
Work with biology, chemistry, ML, engineering, and lab teams to design, debug, and improve high-throughput LC-MS/MS assays.
Extract actionable biological insights from mass spec data, including pathway-level changes, metabolic signatures, lipid remodeling, protein abundance changes, and evidence for specific toxicity mechanisms.
Help build datasets that connect chemical structure, dose, exposure, cellular phenotype, biochemical state, and human toxicity outcomes.
Develop quality control systems for high-throughput mass spectrometry datasets, including instrument performance, sample quality, replicate concordance, batch effects, missingness, drift, and annotation confidence.
Collaborate with ML researchers to build models that use mass spec features to improve toxicity prediction.
Investigate where mass spec helps explain model errors, reveals missing biology, or identifies mechanisms not visible from imaging, transcriptomics, or standard biochemical assays.
Design new strategies for expanding Axiom’s mass spec data generation based on model performance, biological coverage, and customer needs.
Help make mass spectrometry data interpretable and useful to drug hunters, toxicologists, and Axiom’s internal AI agents.
What we are looking for:
We are looking for someone who can combine mass spectrometry expertise, computational depth, and biological judgment.
You might be a great fit if:
You have built computational workflows for untargeted LC-MS/MS metabolomics.
You have used mass spectrometry data to answer real biological questions, not just run pipelines.
You understand the messy reality of mass spec data: missingness, batch effects, adducts, isotopes, retention time drift, annotation uncertainty, instrument artifacts, and biological confounders.
You are comfortable moving from raw files to biological interpretation.
You can reason about metabolism, pathway disruption, lipid biology, protein changes, and drug-induced cellular stress.
You are excited by the idea of using mass spec data as training data for AI systems.
You want to build scalable infrastructure, not just analyze one-off datasets.
You care deeply about data quality, reproducibility, and scientific rigor.
You can work closely with wet lab scientists to improve experimental design and debug assays.
You want ownership over a critical scientific modality at an early company.
You are motivated by the mission of replacing animal testing and preventing clinical toxicity failures.
About Axiom
Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.
What you will do
You will help define Axiom’s core ML research agenda and build the models that power our product.
Define end-to-end ML and agent systems spanning wet-lab data generation, data cleaning, feature extraction, representation learning, model training, evaluation, inference, deployment, and customer-facing outputs.
Build novel models that learn the relationship between chemistry, biological response, dose, exposure, and human toxicity.
Train large multimodal models on paired chemical structures, high-content cellular images, transcriptomics, proteomics, mass spectrometry, ADME, and clinical outcome data.
Develop foundation models and representation-learning systems for biological images, molecules, and multimodal experimental readouts.
Architect models that predict human toxicity as a function of dose, Cmax, in vitro potency, chemical structure, and biological state.
Develop new ways to aggregate, pool, align, and interpret embeddings across assays, doses, timepoints, modalities, compounds, and biological systems.
Work on contrastive learning, self-supervised learning, semi-supervised learning, multimodal learning, graph neural networks, biological image models, generative models, and mechanistic reasoning systems.
Build models that can generalize across chemical space, mechanisms, targets, assays, and customer programs.
Conduct rigorous error analysis to understand when models fail, why they fail, and what data would make them better.
Collaborate with computational biologists, chemists, mass spec scientists, data engineers, and wet-lab teams to design experiments that maximally improve model performance.
Help build Axiom’s mechanistic agents: systems that reason over experimental data, compare compounds to mechanistic neighbors, explain toxicity mechanisms, and guide scientific decisions.
Own the research-to-product loop: prototype, train, evaluate, ship, observe real usage, improve, and repeat.
Ship insanely great models and products to customers.
We are especially interested in people excited by:
Multimodal ML across chemistry, cellular imaging, transcriptomics, proteomics, mass spectrometry, ADME, and clinical outcomes.
Reasoning over massive amounts of multimodal experimental data, model outputs, literature, and mechanistic evidence.
Reinforcement learning for basic biology, chemistry, and advanced drug discovery.
Self-supervised and semi-supervised learning on high-content imaging and biological readouts.
Uncertainty estimation, calibration, and confidence for scientific decision-making.
Mechanistic interpretability for biological and chemical models.
Evaluation systems for models that must perform on real drug discovery problems, not toy benchmarks.
What we are looking for
We are looking for someone with exceptional ML talent, strong engineering ability, and the ambition to become a leader in AI for biology and drug discovery.
You might be a great fit if:
You have done at least one piece of work, in industry, academia, open source, or independently, that shows exceptional machine learning ability.
You are deeply technical and comfortable writing PyTorch, debugging training runs, working with messy data, scaling inference, and building real systems.
You are excited by non-standard, thorny modeling problems where the data is noisy, multimodal, sparse, biased, biological, and deeply important.
You want to work on ML problems where better models can directly change scientific and clinical decisions.
You are not afraid of the data dirty work required to make models better.
You can move between research ideas and production systems.
You care about evaluation, calibration, failure modes, and real-world usefulness.
You are curious enough to learn biology, chemistry, toxicology, pharmacology, and drug discovery.
You want to grow as both a researcher and an entrepreneur.
You want your work to become a product that customers love and rely on.
Technical skills we value
We do not expect every candidate to have all of these, but we are especially excited by experience with:
PyTorch, JAX, TensorFlow, or other deep learning frameworks.
Python, NumPy, Pandas, Polars, PyArrow, scikit-learn, and scientific computing.
Training and evaluating deep neural networks at scale.
Representation learning, embeddings, contrastive learning, metric learning, and self-supervised learning.
Computer vision models, especially for biological imaging, microscopy, cell painting, or high-content screening.
Multimodal ML across images, molecules, text, omics, mass spec, or tabular data.
Large-scale model training, distributed training, GPU infrastructure, inference pipelines, and cloud compute.
Model evaluation, ablations, benchmarking, uncertainty estimation, calibration, and interpretability.
LLMs, agents, retrieval, tool use, and reasoning systems.
Biology, chemistry, toxicology, pharmacology, or drug discovery datasets.
About Axiom
Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.
What You Will Do
You will own the commercial strategy and operations.
You will:
Work with the founders and team to map the people and phased progression strategy for every major account including top pharmaceuticals and AI research labs
Own pilot operational execution and pilot-to-revenue conversion across Axiom’s commercial pipeline
Partner closely with the CEO and technical leadership to turn customer excitement into durable enterprise adoption for companies ranging in size from $100B to over $1T
Chart the course for major accounts going from 6 figures to 7 figures to 8 figures and beyond
Build relationships across scientific, technical, business, legal, procurement, and executive teams
Build and run a rigorous commercial operating system: pipeline management, forecasting, pricing, packaging, deal strategy, and expansion
Act as the voice of the customer inside Axiom, bringing energy, urgency, debate, and insight from the field back into product, roadmap, and company direction
Drive creative dealmaking across SaaS, platform licenses, strategic research collaborations, drug discovery partnerships, and data relationships
Partner with the CEO as a true counterpart, shaping strategy, refining company direction, and pushing decisions through high conviction, intense debate
Be deeply present in person, in the office, with customers, and at conferences build deep relationships with all parts of the organizations to drive customer outcomes
Handle all the dirty work and operations by pushing deals through legal, procurement, and IT while also ensuring the Axiom team is executing flawlessly
Help ensure the trains run on time internally across the highest priority customer and company initiatives
What We Are Looking For
A world-class operator with a strong commercial, operations, and strategy background with a track record of driving complex enterprise relationships to real revenue
Deep understanding of pharma and biotech, including discovery workflows, toxicology, DMPK, and how large organizations evaluate and adopt new platforms
Exceptional at customer management, commercial strategy, and internal coordination / operations
Works extremely hard and sees their work as being a major part of their identity.
Strong product instinct and customer empathy with a natural ability to bring customer needs back into the company
Deep, innate curiosity for the science and software
Highly intelligent, highly energetic, highly persuasive, and highly ambitious.
Able to hold conversations with some of the most successful drug hunters and AI researchers in the world
Strong technical chops Able to fluently and credibly discuss AI, scientific product vision, and business strategy with customers, executives, and internal technical teams.
Creative dealmaker who can structure non-obvious partnerships and get difficult deals done
Strong operator who can introduce rigor, accountability, and clarity without creating bureaucracy.
Comfortable pushing back on the CEO and leadership team with sharp, well-thought opinions.
Thrives in an intense, high expectation, in person, honest, blunt environment.
Based in San Francisco, with regular travel to customers and conferences.
About Axiom:
Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.
What you will do:
Own the harness: the scaffolding, tooling, and infrastructure that turn frontier models into agents that do long-horizon scientific analysis
Build the data backbone that gets everything to the right place: pipelines, storage, and systems for runtime context, agent trajectories, eval results, and training data
Build sandboxed execution environments with instant spin-up/tear-down, reproducible and deterministic enough to trust for evals and RL
Design and run the eval systems: offline suites, test cases on production traces, LLM-as-judge pipelines, regression gates
Work closely with domain experts and encode their taste into rubrics, golden sets, and review workflows, turning \"I know it when I see it\" into something measurable
Build the tools the agent needs to do better work, and stay tuned in to the state of the art for new methods, protocols, and patterns worth adopting
Make every agent run observable and replayable: trace every model call, tool call, and state transition, and build the debugging tooling to make sense of it
Engineer the context: memory, compaction, retrieval, and recovery so long-horizon agent runs stay coherent across hours and crashes
Own the loop itself: retries, budget caps, stop conditions, output verification, permissions, and guardrails
Support ML research with environments, reward instrumentation, and rollout infra for RL on agentic tasks
Expertise:
Python, Modal, DuckDB, FastAPI, Docker, Containerization, Terraform
Engineers who've built with LLM APIs and shipped agentic systems: tool use, loops, and the debugging scars to prove it
Built bespoke evaluation, monitoring, and RL env observability tooling (SvelteKit, Svelte 5, React)
What we look for:
Can tackle deep technical challenges and own/ship simple, clean, maintainable code
High ownership: owns outcomes end to end, not tickets, and doesn't wait for a spec to start moving
Allergic to complexity: reaches for the simplest system that works and keeps it that way as it scales
Strong software engineer first, with infrastructure, platform, data, or devtools depth and production systems they're proud of
Instinctively asks \"how would we know if this is working?\" and builds the measurement alongside the feature
Reads an agent failure trace the way other engineers read a stack trace
Cares about reliability because they know environments that break silently poison evals and training data
Has a knack for surfacing the important questions about what the agent actually needs to do better work
Sharp and confident
keeps up with a really technical & sophisticated crowd
comfortable getting in over their head and figuring it out as they go
thrives in a discipline with no playbook, because it's being invented right now
Curious about how things work: an engineering/tinkering mindset, good at scavenging the state of the art
Passion for learning what \"good\" looks like from deep domain experts and turning it into systems
About Axiom:
Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.
What you will be doing:
Lead Axiom’s evolution into a world-class engineering company focused on enterprise ML software
Design and build the core infrastructure that powers Axiom’s enterprise ML systems, including model evaluation/deployment, model inference/serving, and customer data management
Architect scalable systems for inference, storage, and retrieval of chemical, biological, and clinical data
Deploy large-scale reasoning agents from research environments into production, integrating them into on-prem customer-facing products and infrastructure
Teach and empower scientists across ML, chemistry, and biology to become great engineers by instilling a great engineering culture
Various expertise which gets us interested:
Built SaaS products that store and process large volumes of customer data.
Worked directly with large enterprise customers and supported their complex software needs
Designed and developed large-scale machine learning systems covering data access, training, evaluation, and deployment
Handled the “messy” parts of ML deployment, such as evaluation pipelines, versioning, and monitoring
Built LLM-powered data systems, with a focus on research workflows and information retrieval
Key criteria:
Strong generalist software engineer with experience across cloud infrastructure,machine learning, backend systems, distributed systems
Enjoys working with enterprise customers and simplifying complex technical solutions to meet their needs
Built and deployed production systems used by large enterprise businesses
Invested in team growth particularly when it comes to building strong engineering culture across the company
Passionate about collaborating with researchers and scientists, helping them become strong engineers
Takes full ownership of the customer experience—deeply focused on reliability and all the ways things can go wrong
Demonstrates relentless
About Axiom:
Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.
Who we are looking for:
We are looking for exceptional drug discovery scientists who also have strong computational and mathematical instincts to help build and grow Axiom’s ecosystem. In this role, they will work directly with great scientists at top pharma and biotech companies, researchers from leading AI labs, and Axiom’s multidisciplinary team to build the experimental datasets, agentic systems, and product capabilities needed to accurately predict human drug outcomes. They will also partner closely with scientists inside leading pharma companies to deploy these agents into real discovery workflows, where they will be critical for informing major go/no-go decisions across many high-value drug programs. We want to hire people who inspire us and level up the entire team. They should be high energy, high agency, and have great taste for what matters. They should have a relentless “observe, orient, decide, act” loop, and be constantly identifying what needs to happen and getting it done. They need to be technically excellent and obsessive masters of their craft, as well as having a great curiosity which will keep them at the frontier of tech and help them interface between AI, engineering, product, biology, chemistry, and business. They could work in big tech or pharma, but it won’t satisfy them. They want to go on an adventure which will be brutally challenging, and to share in the rewards and satisfaction at its end.
Key Responsibilities:
Build and ship agentic tools and data systems that let agents accurately predict human drug outcomes
Own key customer deliverables end to end, including data generation, QC/QA, computational analysis, agentic workflows, data presentation, and partnership with sponsors to translate results into drug program decisions.
Partner directly with drug hunters at top pharma companies, researchers at leading AI labs, and Axiom’s multidisciplinary team to build the experimental datasets and agentic systems needed to accurately predict human outcomes, then deploy those systems into real discovery workflows to inform major go/no-go decisions across high-value drug programs.
Work closely across Axiom’s chemistry, biology, machine learning, engineering, and lab teams to embed each customer’s goals and context into our work, and own the feedback loop from customer needs back into Axiom’s data generation, models, and agentic workflows.
Ensure Axiom learns from every customer engagement by expanding and productionizing the insights generated by our agentic workflows, while reducing analysis cycle time for each subsequent customer.
Conduct independent analyses of Axiom’s data to improve underlying data quality and develop new analytical frameworks that can be scaled through agentic workflows.
Enable leading pharma drug hunters to define the future of drug discovery using Axiom’s agents, data, and experimental platform.
Expertise which sparks our interest:
Demonstrated experience with drug discovery programs, especially decision-making in hit-to-lead, lead-op, or candidate nomination.
Medicinal chemistry reasoning around structure–activity and structure–property relationships, matched molecular pairs, and hit-to-lead / lead-optimization design.
ADME/DMPK and PK/PD interpretation, physicochemical property optimization, and exposure–response reasoning.
Strong computational chemistry, engineering, biochemistry, and ML ability.
Presentation (e.g. slides, reports) and soft skills, with the ability to own customer relationships and partnerships with both biotech companies and big pharma.
Key criteria:
Comfortable in both customer-facing and technical roles — you can identify a customer’s technical needs, translate them into an Axiom experimental and analytical plan, then guide the execution and communication of results.
Likely an advanced degree in chemistry, cheminformatics, toxicology, or computational chemistry. With significant experience in drug discovery and a proven track record of advancing programs towards development
Comfortable with Python, cheminformatics libraries like RDKit, scientific computing libraries like NumPy, and data processing such as SQL and Pandas.