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Handshake's mission is to organize expert human knowledge to advance the AI economy. Handshake AI works directly with frontier labs on their most consequential data, evaluation, and post-training challenges, building systems that turn expert human knowledge into data and evaluations that make frontier models better.
Handshake Labs is building external AI products, research platforms, and customer-facing AI systems, evolving custom work into reusable products and platforms. The 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.
In this role, you will help build the data systems that make frontier model training possible. The data Handshake builds and acquires is becoming increasingly complex and sensitive. You will be responsible for improving how data is generated, processed, and prepared—whether building higher-quality synthetic and LLM-generated training data, or making acquired third-party data safe to use by removing personal information while preserving valuable structure.
You will partner with researchers, domain experts, legal/compliance stakeholders, and customers to translate ambiguous data questions into experiments, pipelines, and durable products. Early team members will have unusual influence over technical direction, standards, and culture.
Key responsibilities include:
- Design and build systems that improve quality, scale, and safety of data generated and acquired 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
- 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 legal/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
REQUIREMENTS:
- 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
- Hands-on individual contributor track record (not a team-lead or engineering-management role)
- 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, and 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 environment with substantial ownership
- Collaborative, low-ego communication and 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 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
