Location: San Francisco, California or Seattle, Washington
Employment Type: Full time, Hybrid
The Machine Learning team — internally known as \"Potato Radius\" — builds the training pipelines, feature infrastructure, and evaluation systems behind every score Sift returns, across more than 700 customers and a trillion-plus events a year. We are Sift's Data Science and ML Engineering team responsible to ship models fast, prove they work, and trust them in production.
We're hiring a Senior Engineering Manager to lead this team. You're a manager who's inspiring and technical, and who knows how to bring focus to what matters now without losing sight of the long term. You value collaboration and transparency, operate with a get-stuff-done mindset, and bring the technical depth and bias for shipping to spot the manual, brittle, or duplicated work that's quietly slowing the team down. You build a culture of mentorship, give regular and constructive feedback, set clear goals, and grow your team by hiring effectively.
Launch a unified model evaluation framework that gives Data Science fast, trustworthy, apples-to-apples comparisons before a model ever reaches production or shadow traffic.
Evolve core feature infrastructure — including a new global feature store — to improve accuracy and unlock faster experimentation.
Bring a fresh approach to model configuration, replacing tribal knowledge and manual gating with auditable, safely-controlled releases.
Introduce agentic, AI-assisted tooling into customer investigations, automating repetitive data pulls and validation so analysts spend their time on judgment calls, not manual digging.
Build automation that detects an active fraud attack, adjusts score calibration in real time, and cleanly reverts once it subsides.
Lead and grow the team: Own the roadmap, execution, and quality of the systems that train, evaluate, and serve Sift's ML models in production, leading a team of ML platform engineers and data scientists.
Stay technical: Review designs, unblock engineers on hard problems, and make credible calls on architecture and trade-offs.
Drive customer POVs: Partner directly with strategic customers and Sales/Solutions Engineering on technical proof-of-value engagements, translating customer requirements into platform capabilities.
Reduce technical debt: Drive a sustained, measurable reduction in technical debt across the ML platform, balancing new feature delivery with the health of existing systems.
Build evaluation frameworks: Mature the systems that give Data Science and ML Engineering fast, trustworthy signals on model quality before and after deployment.
Automate the ML lifecycle: Identify repeatable, manual processes across training, evaluation, deployment, and monitoring, and drive their automation.
Partner cross-functionally: Align platform investments with business priorities alongside Data Science, Core Infrastructure, Product, and Customer Success.
GCP, AWS, Spark, Kafka, Kubernetes, Docker, Databricks, Python
8+ years of overall hands-on engineering experience, including 4+ years managing software, data science or machine learning engineering teams.
Experience managing Data Scientist and/or in-depth knowledge for data science.
Deep technical fluency in machine learning systems: model training pipelines, feature engineering, model serving, and evaluation at production scale.
Proven track record leading technical customer engagements or POVs, including direct interaction with enterprise customers.
Demonstrated success reducing technical debt in a live, high-traffic production system without stalling feature delivery.
Experience designing or scaling evaluation frameworks (offline and/or online) for machine learning models.
Track record of identifying manual, repeatable engineering processes and driving their automation.
Experience hiring, mentoring, and developing engineering talent.
B.S. or MS/Phd in Computer Science (or related technical discipline), or equivalent practical experience.
Experience with large-scale distributed ML infrastructure such as Spark, Flink, Databricks, or similar.
Familiarity with fraud detection, risk, or trust & safety domains.
Hands-on experience with GCP or AWS ML infrastructure.
Experience with streaming architectures (e.g., Kafka) and containerized/orchestrated deployments (Docker, Kubernetes).
Familiarity with using AI coding assistants (e.g., Claude Code) to accelerate development.
Introduction interview: 30- 45 minutes with a recruiter to discuss your background and the role.
Hiring Manager interview: 30- 45 minutes with the hiring manager to explore your fit for the position.
Hybrid onsite loop with the team: approximately 4–5 hours covering system design, a technical deep dive, a cross-functional stakeholder scenario, and values & behavior.
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Brands including DoorDash, Yelp, and Poshmark rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.
Competitive total compensation package
401k plan
Medical, dental and vision coverage
Wellness reimbursement
Education reimbursement
Flexible time off
Let’s build it together:
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.
About the Team:
We’re people that are passionate about making the internet a safer and more trusted place for all. We love the fraud and trust & safety space and want to teach companies how they can protect themselves, their users and create frictionless experiences for legitimate consumers. As a Forward Deployed Engineer, Trust and Safety, you are heavily experienced in detecting and acting on multiple types of online abuse from a technical and quantitative perspective. You’ve helped build tools, models and detection platforms at companies that have had to work through these threats at a global level.
What you’ll do:
Work with our Trust and Safety Architect and Data Science teams to surface emerging fraud patterns across the network escalate and proactively take them down.
Detect patterns and turn those findings into sharper signals, tighter configurations, and smarter decisioning logic.
Work across different verticals and closely with customers, partners and prospects with different risk appetites - some optimizing for approval rates, some minimizing chargebacks, some fighting account takeover and other types of abuse.
Help build dashboards, tune and build models, decision logic and custom signals to help customers achieve their desired business outcomes
Identify sources of false positives, possible coverage gaps and other vulnerabilities by digging into raw event streams; form a hypothesis, design a test and implement the fix
Lead forensic investigations during fraud spikes: trace attack patterns to their source, identify the technique being used, deliver a clear writeup with remediation steps
Distinguish between one-off anomalies and systemic gaps that indicate a product opportunity - and advocate for the latter with rigor
Contribute to detection frameworks, investigative tooling, and internal playbooks that make every engineer and analyst at Sift more effective
Be the conduit between customer reality and internal roadmap; your field observations should directly accelerate what Sift ships next
Some travel may be required
Required
5 - 8 years in fraud, trust & safety, risk, or a closely related data science domain - you've spent meaningful time working with fraud data, not just adjacent to it
Strong SQL and Python skills; you reach for code to answer a question, not to build a pipeline
Strong understanding of ML concepts applied to fraud: classification models, feature engineering, precision/recall tradeoffs, threshold calibration, score drift
Experience analyzing large-scale behavioral or transactional datasets to find patterns and anomalies - you know what a fraud ring looks like in the data, not just in a textbook
Ability to communicate technical findings to both technical and non-technical stakeholders; you can write a forensic investigation report and present it to a VP of Risk in the same week
Customer-facing experience; you understand that different businesses have different priorities, and that listening before optimizing is part of the job
Nice to Have
Hands-on experience with fraud detection platforms (in house or 3rd party)
Hands-on experience building with AI: LLM APIs, prompt engineering, or agentic workflows - whether that's automating an investigation step, building a tool that surfaces patterns from raw data, or wiring together a multi-step agent to accelerate fraud analysis
Familiarity with real-time event processing systems
Experience with rules-based decisioning systems alongside ML - knowing when a hard rule beats a model score
Background in payments, e-commerce, fintech, marketplace, or account security fraud
Prior forward deployed, staff engineering, or embedded consulting experience at a technical product company
Computer Science, Data Science, Mathematics, Statistics, Information Systems, Economics degree or equivalent
Let’s build it together:
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.
As a Machine Learning Engineer at Sift, you will bridge the gap between data science and large-scale distributed systems. You won’t just train models in isolation; you will build end-to-end pipelines that extract signals, train custom models per merchant, and serve predictions at production scale with low latency. You will work on an automated machine learning ecosystem that dynamically recalibrates models based on streaming global telemetry data.
Model Development & Refinement: Design, build, and deploy online machine learning models (including ensemble methods, deep learning, transformer architectures and graph-based models) to catch evolving fraud vectors in real time.
Feature Engineering at Scale: Engineer high-frequency time-series features from over 1 trillion behavioral events, optimizing for low-latency signal extraction and pattern recognition.
Production MLOps: Maintain and enhance our automated model training and deployment infrastructure, ensuring frictionless continuous integration and continuous deployment (CI/CD) of newly trained models.
System Optimization: Write high-performance code to minimize scoring latency at runtime, ensuring our core ML services scale seamlessly across distributed databases.
Collaborative Innovation: Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate business-level fraud patterns into robust algorithmic solutions.
Experience: 4+ years of professional experience building and deploying large-scale machine learning models into high-traffic production environments.
Solid Programming Foundations: Strong proficiency in Java or Scala (for our production backend) as well as Python (for data analysis and model prototyping).
Distributed Systems & Big Data: Practical experience with Databricks and big data processing frameworks like Apache Spark, Apache Flink, or Hadoop, and working with NoSQL data stores like Bigtable.
Strong Mathematical Foundations: Deep understanding of statistical modeling, probability, and standard machine learning algorithms (e.g., XGBoost, Random Forests, Neural Networks, and Clustering techniques).
System Design Mentality: Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP).
Experience explicitly in the fraud detection, risk mitigation, or cyber-security domains.
Deep knowledge of streaming architectures (e.g., Apache Kafka).
Familiarity with containerization and orchestration tools like Docker and Kubernetes.
Familiarity with leveraging AI coding assistants (e.g., Claude Code) to accelerate development and model prototyping
Please note: final stage candidates may be asked to travel for in-person final round interviews.
Let’s build it together:
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.
About the team:
The Security Engineering team is responsible for protecting Sift’s products, infrastructure, and data while enabling our engineering organization to ship quickly and safely. We embed with product and platform teams, build and run security tooling, and design controls that scale across our cloud‑native environment. As a Senior Security Engineer, you’ll be a key technical contributor and subject‑matter expert, working on projects that materially reduce risk and strengthen Sift’s security posture.
Role:
In this role, you will design, implement, and operate security controls and tooling across Sift’s stack. You’ll work closely with Engineers, SREs, IT, and Legal/Compliance to secure our systems end‑to‑end—from application code and CI/CD pipelines to cloud infrastructure and identity. You will also help define our standards, mentor other engineers on secure practices, and contribute directly to audits and compliance efforts.
What you’ll do:
Design and implement security controls and tooling across Sift’s infrastructure and applications (e.g., IAM policies, network controls, secrets management, endpoint protections, container and workload security).
Embed with product and platform teams to perform security design reviews, threat modeling, and code or configuration reviews for new features and services.
Improve the secure SDLC by integrating AI-powered scanning tools, security scanning (SAST/DAST, dependency and container scanning) into CI/CD, and by developing guardrails, templates, and best practices for engineers.
Own or co‑own vulnerability management workflows, from discovery and triage through remediation, including defining SLAs, coordinating with service owners, and tracking closure.
Develop automation (scripts, services, integrations) to detect misconfigurations, anomalous activity, or policy violations, and to reduce manual operational work for the security team.
Participate in security incident response (on‑call rotation or escalation), including investigation, containment, root cause analysis, and long‑term fixes.
Contribute to security documentation and standards, ensuring we have clear, actionable guidance for engineers on topics like authentication, authorization, data encryption, and key management.
Support audits and assessments (e.g., SOC 2, customer security questionnaires) by providing technical details and evidence of control design and effectiveness.
Mentor other engineers on secure design and implementation practices through pairing, reviews, training sessions, and written guidance.
What will make you a strong fit:
5+ years of experience in security engineering, infrastructure engineering, or application security, ideally in a B2B SaaS or cloud‑native environment.
Hands‑on experience with at least one major public cloud platform (e.g., GCP, AWS), including IAM, networking, logging/monitoring, and security services.
Strong proficiency in at least one programming or scripting language (e.g., Python, Go, Java, or similar) and experience using code to automate security controls or detection.
Direct experience with AI/LLM-specific security risks (prompt injection, model supply chain, etc.)
Demonstrated knowledge of secure application and system design, including topics like authentication/authorization, encryption in transit and at rest, least‑privilege access, and secrets management.
Experience with security tooling such as vulnerability scanners, SAST/DAST tools, SIEM/centralized logging, endpoint protection, or cloud security posture management.
Solid understanding of common vulnerabilities and attack patterns (e.g., OWASP Top 10, misconfigurations, supply‑chain risks) and how to mitigate them in practice.
Ability to work cross‑functionally with engineering, IT, and compliance/legal teams, and to translate security requirements into practical implementation details.
Clear written and verbal communication skills, including the ability to document designs and decisions and to educate others on security best practices.
A collaborative, pragmatic approach: you’re comfortable making risk‑based decisions, proposing options, and supporting teams in implementing secure, scalable solutions.
Let’s build it together:
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.
As a Customer Solutions Engineer at Sift, you are the bridge between the customer-facing team and the engineering and product teams. You explain complex ideas to customers and relay customer needs to engineering and product. When a customer’s needs and our product don’t fully align, you’re the internal customer advocate who drives critical solution changes and refinements to our offerings.
What you’ll do:
Own the most complex customer issues: Customer Solutions Engineering is a team of highly technical customer-facing experts. You’ll partner closely with our Customer Success Management as the technical liaison to Enterprise/Strategic accounts, and make sure they maximize Sift value. This team is responsible for building a deep understanding of customers’ business problems, technical requirements and success criteria -- empowering customers to solve complex business problems.
Support the customer lifecycle: You will be the customer’s technical point of contact throughout their relationship with Sift, while also partnering with our Support team. You’ll understand the customer's business and technical needs. Day to day this involves understanding the nuances of their integration, working alongside Sift’s data science team to ensure the customer’s machine learning model health, meticulously whiteboarding the flow of their data to expand their integration, and being the internal voice of the customer. In short, you’ll do whatever it takes to make the customer successful.
Automate and Optimize: You will apply your deep technical and customer knowledge to help iterate on and improve internal and external processes. For some customers, you’ll write custom queries and scripts to calculate metrics to prove ROI, and optimize recurring processes. Internally, you will automate tasks, and take innovative approaches to problem-solving that will have the power to help guide internal decision-making.
What would make you a strong fit:
5+ years in a technical customer-facing role
Close cross-functional collaboration with data science, engineering and product
Outstanding communication skills, including the ability to communicate complex technical and business concepts to developers and non-developers
Creative, coachable, collaborative, goal-oriented, and thoughtful. You have a strong work ethic and are committed to excellence
You have a growth mindset - you enjoy learning new skills, and applying it to technical challenges
You are resourceful and proactive. If something seems strange, you’ll investigate. You are not afraid of challenging the status quo
Strong experience writing scripts and queries to build internal tools and data pulls (For example: SQL, Python, Bash scripting) and comfort with APIs and web technologies
Ability to prioritize, and deliver on multiple projects in tandem. You thrive under pressure and enjoy clarifying ambiguous situations
Bonus points for experience in the B2B payments and/or fraud space
Please note: Final round interviews may be held in person
Let’s build it together:
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.
Sift is hiring a Contracts and Legal Operations Specialist to run our contracting process end to end, including customer, vendor, and partner agreements. AI tools will handle first-pass review and drafting on routine paper; you take it from there, negotiating and closing standard agreements independently and escalating anything complex or high-risk to legal counsel. You'll also own the contract automation and playbooks going forward, tuning them as the business changes. The role works closely with Deal Desk, Sales, and Partners from a contracts perspective, along with finance, security, privacy, marketing, and product.
• Draft, negotiate, and close standard commercial agreements independently, including NDAs, order forms, standard renewals, and low-risk vendor and partner agreements, from first draft to signature.
• Review and finalize AI-generated first-pass redlines and clause extraction on incoming contracts before they go out to counterparties.
• Own the contract automation and playbooks over time: update clause libraries, adjust intake logic, and add new agreement types as the business changes.
• Manage vendor and procurement requests end to end: negotiate standard pricing and terms, track requests through Jira, and push back on terms that don't work for Sift.
• Manage the full contract lifecycle: intake, repository, renewals, approvals, and terminations.
• Coach Sales on standard terms and redlines so routine deals close without legal touching every file.
• Collaborate with Sales and Deal Desk to streamline the sales process and related deal cycles.
• Track and report on contract and vendor metrics.
• Triage inbound contracts and vendor requests, and escalate anything complex, high-risk, or outside standard terms to legal counsel.
• Take on other duties as part of a small, fast-moving legal and compliance team.
• 2+ years of experience in a contracts, legal operations, or deal desk role, ideally at a B2B SaaS company. No law degree required.
• Comfortable using AI tools daily to speed up drafting and review, not just occasionally.
• Good judgment on what's standard versus what needs to go to legal counsel.
• Experience with Salesforce and Jira.
• Strong organizational and time-management skills, with the flexibility to handle volume and competing requests.
• Comfortable working in a role that's still being built, where the process and tooling will keep changing.
• Prior legal, paralegal, or compliance experience.
• Experience with a CLM platform (e.g.,DocuSign Navigator, Ironclad, LinkSquares, Conga).
• Exposure to data privacy or DPA concepts (CPRA, GDPR).
• Experience in a fraud, risk, or trust & safety adjacent business.
• Hands-on experience using LLM tools (e.g., Claude, ChatGPT) to build things, not just chat: custom prompts, workflows, or lightweight automations.
• Prior experience working in a high-growth company.
Let’s build it together:
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.
As a Director or Sr. Director of Sales at Sift, you are the strategic catalyst driving our revenue growth across top-tier enterprise accounts. You lead and inspire a high-performing sales team, instilling operational rigor while navigating complex enterprise deals and executive relationships. You act as the bridge between field sales execution, executive leadership, and cross-functional partners in Product, Marketing, and Customer Success ensuring our go-to-market strategy aligns with customer needs to scale predictable revenue.
We are looking for a proven enterprise sales leader with a track record of driving complex sales cycles, multi-threading across executive buyers, and instilling operational rigor through MEDDPICC and Command of the Message. You excel at fostering a culture of high execution standards, continuous coaching, and mutual accountability bringing the strategic vision, executive presence, and sales coaching needed to scale top-line revenue and develop world-class sales talent.
Your goal is to drive adoption and partner with our customers as a trusted advisor while building a high-accountability sales team that consistently wins.
Drive Enterprise Revenue & Pipeline Growth: Lead complex sales cycles, enforce multi-threading across executive and technical buyers, and deliver predictable quarterly revenue performance.
Enforce Sales Rigor & Methodology: Operationalize MEDDPICC for deal qualification and Command of the Message for value positioning, conducting deep deal reviews and holding reps accountable to forecast accuracy.
Shape GTM Strategy: Partner with executive leadership to refine enterprise GTM strategy, target account segmentation, and competitive positioning across the digital trust and safety ecosystem.
Coach and Scale Talent: Mentor Enterprise Account Executives through active coaching, joint customer calls, and structured performance management.
Executive Sponsorship & Alignment: Serve as executive sponsor on key enterprise opportunities while aligning customer needs with Product, Marketing, and Customer Success.
Enterprise Sales Leadership: 8–10+ years of Enterprise SaaS sales experience, including 3–5+ years leading and scaling high-performing enterprise sales teams with a track record of consistently exceeding quotas in complex, six-figure+ deal environments.
Methodology Mastery: Operational discipline in applying MEDDPICC deal qualification and Command of the Message value positioning frameworks.
Complex Multi-Threading: Proven ability to execute multi-threaded sales strategies across C-suite executives, Risk/Security leaders, and Finance stakeholders.
Accountability & Coaching Culture: Demonstrated track record of establishing high performance standards, coaching reps, and managing underperformance effectively.
Strategic GTM Vision: Ability to analyze market trends, navigate competitive SaaS landscapes, and collaborate cross-functionally to drive growth.
United States:
Competitive total compensation package
401k plan
Medical, dental, and vision coverage
Flexible lifestyle wallet
Learning & development wallet
Flexible time off
Let’s build it together:
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.