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Handshake is hiring a Member of Technical Staff to build data systems that enable frontier model training. The company operates Handshake AI, a rapidly growing business that works directly with leading AI labs, Fortune 500 partners, and educational institutions to create evaluations, publish benchmarks, and advance AI capabilities. Handshake Labs focuses on external AI products, research platforms, and customer-facing AI systems, evolving custom work into reusable products and platforms.
In this role, you will design and build systems that improve the quality, scale, and safety of data generated and acquired for frontier model training. Your responsibilities include:
- Design and build systems spanning synthetic data generation, data anonymization, and PII removal to improve data quality, scale, and safety.
- Translate ambiguous research, partner, and compliance needs into clear hypotheses, experiments, evaluation plans, and production implementations.
- Build and improve data-processing pipelines, evaluation frameworks, benchmarks, and quality-control systems.
- Run fast iteration loops: prototype, evaluate, interpret results, and turn learnings into the next system or product.
- Partner directly with researchers, domain experts, 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.
This is a hands-on individual contributor role (not team-lead or management). You will have unusual influence over technical direction, standards, and culture as an early team member.
REQUIREMENTS:
- 2–10 years of 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.
- Hands-on individual contributor track record with strong Python skills and 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, distinguishing signal from noise.
- Experience designing systems (not only implementing specifications), including tradeoffs around quality, scale, reliability, and reuse.
- Comfort operating in ambiguous, fast-moving environments with substantial ownership.
- Collaborative, low-ego communication and ability to work effectively with researchers, engineers, domain experts, and customers.
Especially compelling experience includes: 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 with relational or graph-structured data, preserving referential integrity); 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 regulated or high-sensitivity data environments (healthcare, finance, HR, 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.
