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Handshake Labs is building external AI products, research platforms, and customer-facing AI systems focused on the post-training loop for frontier models. The company has grown Handshake AI from $0 to ~$1B run rate in 2025, working directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of AI data.
As a Member of Technical Staff, Post-Training, you will help define and build this new organization in a broad, high-ownership role. You will partner with researchers, domain experts, and customers to turn ambiguous post-training questions into experiments, evaluation frameworks, data pipelines, and products. Early members of the team will have unusual influence over technical direction, operating culture, and the reusable systems built.
Key responsibilities include:
- Design post-training systems and methodologies for frontier models, including supervised fine-tuning, reinforcement learning, preference optimization, reward modeling, and related approaches
- Translate open-ended research or partner needs into clear hypotheses, experiments, evaluation plans, and production-quality implementations
- Build and improve evaluation frameworks, benchmarks, training environments, data-processing pipelines, 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 AI researchers and domain experts to develop high-signal data, feedback, and evaluation methods
- 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
- Contribute to the field through benchmarks, open-source tools, research, and technical writing where it creates leverage
The role values demonstrated research capability, technical judgment, and a builder's mindset over specific titles or degrees. You may come from research science, research engineering, machine learning engineering, or a closely related background.
REQUIREMENTS:
- 3+ years of demonstrated strength in post-training, fine-tuning, or model-evaluation work (relevant experience includes RL, SFT, LoRA/PEFT, full fine-tuning, RLHF, DPO, PPO, reward modeling, or training environments)
- Strong Python skills and ability to write clean, efficient, scalable software
- Hands-on experience with modern ML tooling, particularly PyTorch and large-scale data, training, or evaluation workflows
- Sound experimental judgment: form hypotheses, choose meaningful metrics, diagnose failures, distinguish signal from noise
- Experience designing systems (not only implementing specifications), including ability to make 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 ML training, inference, data, or evaluation 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
- Published research, meaningful open-source contributions, or evidence of technical leadership in ML systems or AI research
- Experience productizing research or repeated customer work into robust, reusable platforms
