Member of Technical Staff, AI Engineering
San Francisco, CA$200k - $350kFull-timePosted 3w ago
About the role
Role focused on building data systems for frontier model training, including synthetic data generation, data anonymization/PII removal, and ensuring data quality, safety, and compliance for high-sensitivity datasets.
Responsibilities
- 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.
Qualifications
- 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.
Skills
Required:PythonData pipeline designData anonymization/PII removalExperiment design
Benefits
- 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
Last updated Oct 15, 2026. Interested? Apply on the company site.
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