Member of Technical Staff, Data AI

Apply; San Francisco, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity ownership

401(k) match

Parental benefits and family support

Medical, dental, vision, mental health

Job summary

Handshake is hiring a Member of Technical Staff to help build data systems for frontier model training. You will design, implement, and improve pipelines that generate and sanitize data for AI research, while working with researchers, legal/compliance, and customers to turn data questions into durable products.

Low-ego, collaborative engineers will join early to shape standards, architecture, and culture as the team tackles high-sensitivity data challenges and scalable data platforms.

Qualifications

• Strong Python skills and the ability to write clean, efficient, scalable software for large, messy datasets.
• Sound judgment for reasoning about data quality, risk, and utility—forming hypotheses and metrics.
• Experience designing systems with tradeoffs around quality, scale, reliability, and reuse.
• Comfort operating in an ambiguous, fast-moving environment with ownership.
• Collaborative, low-ego communication with researchers, engineers, domain experts, and customers.

Responsibilities

• Design and build data systems for high-quality synthetic and de-identified data for frontier model training.
• Translate research, partner, or compliance needs into hypotheses, experiments, and production implementations.
• Build and improve data-processing pipelines, evaluation frameworks, benchmarks, and quality-control systems.
• Run rapid iteration loops from prototype to production, interpreting results and applying learnings.
• Partner with researchers, domain experts, and legal/compliance teams to ensure data is high-utility and responsibly handled.
• Identify repeatable patterns and productize them into reusable software and platforms.

Skills

Python

Data quality

System design

Ambiguity handling

Collaboration

Job description

About Handshake

Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions.

In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We’ve grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month.

Why join Handshake now:

• Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel

• Partner hand-in-hand with world-class AI labs, Fortune 500 partners and the world’s top educational institutions

• Work together with engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders

• Build a massive, fast-growing business with billions in revenue

About Handshake Labs

Handshake Labs is building external AI products, research platforms, and customer-facing AI systems. We are evolving work that is often custom-built for an individual partner into reusable products and platforms that improve with every deployment.

Our work spans the full post-training loop: designing evaluations and training environments, building high-quality data and feedback systems, running experiments, and turning what works into durable infrastructure. For example, we are developing agents that can analyze long, complex coding-agent sessions in days rather than weeks—with expert review and calibration built into the system.

The Role

We are hiring a Member of Technical Staff to help build the data systems that make frontier model training possible. The data Handshake builds for and acquires on behalf of labs is getting more complex and more sensitive, and this role is responsible for improving how we generate, process, and prepare that data—whether that means building higher-quality synthetic and LLM-generated training data, or making acquired third-party data safe to use by removing personal information while preserving the structure that makes it valuable.

You will partner with researchers, domain experts, legal/compliance stakeholders, and customers to turn ambiguous data questions—about generation, quality, evaluation, or privacy—into experiments, pipelines, and durable products. Early members of the team will have unusual influence over our technical direction, standards, and culture.

Location: San Francisco & Mountain View preferred; open to exceptional candidates in other locations (London, Canada, Bangalore, etc.)

What you’ll do

• Design and build systems that improve the quality, scale, and safety of the data Handshake generates and acquires for frontier model training—spanning synthetic data generation and data anonymization/PII removal.

• Translate ambiguous research, partner, or compliance needs into clear hypotheses, experiments, evaluation plans, and production-quality implementations.

• Build and improve data-processing pipelines, evaluation frameworks, benchmarks, and quality-control systems, whether the goal is generating higher-signal synthetic data or verifying that sensitive data has been properly de-identified.

• Run fast, rigorous iteration loops: prototype, evaluate, interpret results, and turn learnings into the next system or product.

• Partner directly with researchers, domain experts, and—where relevant—legal and compliance teams to ensure data is both high-utility and responsibly handled.

• Identify repeatable patterns across engagements and productize them into reusable software and platforms.

• Raise the technical bar through strong design judgment, clear communication, code quality, and mentorship.

What we’re looking for

• 2-10 years of recent, demonstrated experience in one or more of: synthetic/LLM-generated data, post-training and model-evaluation work, privacy engineering, or data anonymization/de-identification at scale.

• A hands-on individual contributor track record—this is not a team-lead or engineering-management role.

• Strong Python skills and the ability to write clean, efficient, scalable software for large, messy, real-world datasets.

• Sound judgment for reasoning about data quality, risk, and utility—forming hypotheses, choosing meaningful metrics, diagnosing failures, and distinguishing signal from noise.

• Experience designing systems—not only implementing specifications—including tradeoffs around quality, scale, reliability, and reuse.

• Comfort operating in an ambiguous, fast-moving environment with substantial ownership.

• Collaborative, low-ego communication and the ability to work effectively with researchers, engineers, domain experts, and customers.

Especially compelling experience

• Building or operating large-scale synthetic or LLM-generated data pipelines for model training.

• Building or operating large-scale data de-identification or anonymization systems, ideally involving relational or graph-structured data, with experience preserving referential/relationship integrity after anonymization.

• Developing LLM/agent benchmarks, evaluation methodologies, annotation systems, or data-quality frameworks.

• Research or applied work on reinforcement learning, alignment, model behavior, synthetic data, or human-in-the-loop systems.

• Prior work in a regulated or high-sensitivity data environment (healthcare, finance, HR/people data, government), or experience with re-identification risk assessment and privacy auditing.

• Published research, meaningful open-source contributions, or evidence of technical leadership in ML systems, data engineering, or AI research.

• Experience productizing research or repeated customer work into robust, reusable platforms.

Why join

• Work on problems at the center of how frontier AI systems improve, alongside leading labs and domain experts.

• Help build an early technical organization where your work shapes the roadmap, standards, and culture.

• Move fluidly from research insight to real-world systems, with the resources and customer context to see those systems matter.

• Join a company building durable infrastructure for careers in the AI economy.

• Perks

  Ownership: Equity in a fast-growing company

  Financial Wellness: 401(k) match, competitive compensation, financial coaching

  Family Support: Paid parental leave, fertility benefits, parental coaching

  Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend

  Growth: $2,000 learning stipend, ongoing development

  Office: Commuting support, free lunch, and gym in our SF office

  Time Off: Flexible PTO, 15 holidays + 2 flex days

  Connection: Team outings & referral bonuses