Member of Technical Staff, AI Engineering

The role

You will design and build systems to improve the quality, scale, and safety of data for frontier model training. This includes synthetic data generation and data anonymization to support research, evaluation, and production-quality implementations.

What you will do

• Design and build systems that improve the quality, scale, and safety of data Handshake generates and acquires
• Translate ambiguous research, partner, or compliance needs into hypotheses, experiments, and implementations
• Build and improve data-processing pipelines, evaluation frameworks, benchmarks, and quality-control systems
• Run fast, rigorous iteration loops to prototype and evaluate results
• Partner with researchers, domain experts, and legal and compliance teams
• Identify repeatable patterns and productize them into reusable software and platforms
• Raise the technical bar through design judgment, communication, code quality, and mentorship

Required qualifications

• 2–10 years of recent, demonstrated experience in 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
• Strong Python skills
• Ability to write clean, efficient, scalable software for large, messy, real-world datasets
• Sound judgment for reasoning about data quality, risk, and utility
• Experience designing systems including tradeoffs around quality, scale, reliability, and reuse
• Comfort operating in an ambiguous, fast-moving environment with substantial ownership
• Collaborative, low-ego communication

Nice to have

• 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
• 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
• 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

Benefits

• Equity in a fast-growing company
• 401(k) match
• Competitive compensation
• Financial coaching
• Paid parental leave
• Fertility benefits
• Parental coaching
• Medical, dental, and vision
• Mental health support
• $500 wellness stipend
• $2,000 learning stipend
• Commuting support, free lunch, and gym in our SF office

About the company

Handshake's mission is to organize expert human knowledge to advance the AI economy. Handshake AI works with frontier labs on data, evaluation, and post-training challenges.