Member of Technical Staff (AI Engineering)

Join Handshake as a Member of Technical Staff (AI Engineering) and play a crucial role in building the data systems that enable frontier model training. You will be responsible for improving data generation, processing, and preparation, while collaborating with researchers, domain experts, legal/compliance stakeholders, and customers. This position offers a unique opportunity to influence the technical direction, standards, and culture of the team.

- Design and build systems that improve the quality, scale, and safety of the data Handshake generates and acquires for frontier model training. - 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.

- A hands-on individual contributor track record—this is not a team-lead or engineering-management role - Comfort operating in an ambiguous, fast-moving environment with substantial ownership - 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 - Sound judgment for reasoning about data quality, risk, and utility—forming hypotheses, choosing meaningful metrics, diagnosing failures, and distinguishing signal from noise - Strong Python skills and the ability to write clean, efficient, scalable software for large, messy, real-world datasets - Experience designing systems—not only implementing specifications—including tradeoffs around quality, scale, reliability, and reuse - Collaborative, low-ego communication and the ability to work effectively with researchers, engineers, domain experts, and customers - Building or operating large-scale synthetic or LLM-generated data pipelines for model training - Developing LLM/agent benchmarks, evaluation methodologies, annotation systems, or data-quality frameworks - Published research, meaningful open-source contributions, or evidence of technical leadership in ML systems, data engineering, or AI research - 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 - 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 - Experience productizing research or repeated customer work into robust, reusable platforms - Research or applied work on reinforcement learning, alignment, model behavior, synthetic data, or human-in-the-loop systems