About Handshake

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 the systems that turn expert human knowledge into the data and evaluations that make frontier models better.

You will work alongside engineers, researchers, operators, and builders from organizations including Scale AI, Meta, Google, Amazon, xAI, Notion, and Palantir—and help build the systems that make expert human knowledge useful for advancing AI.

About Handshake Labs

Handshake Labs is building external AI products, research platforms, and customer-facing AI systems. We are evolving work that is often custom-built for an individual partner into reusable products and platforms that improve with every deployment.

Our 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. For example, we are developing agents that can analyze long, complex coding-agent sessions in days rather than weeks—with expert review and calibration built into the system.

The Role

We are hiring a Member of Technical Staff, Post-Training to help define and build this new organization. This is a broad, high-ownership role for researchers who build. You may come from research science, research engineering, machine learning engineering, or a closely related background; what matters is the ability to reason deeply about model improvement and turn that reasoning into reliable systems.

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 our technical direction, operating culture, and the reusable systems we build.

We care more about demonstrated research capability, technical judgment, and a builder’s mindset than a specific title, degree, or career path.

Location: San Francisco & Mountain View preferred; we are open to exceptional candidates in other locations.

What you’ll do

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.

What we’re looking for

3+ years of demonstrated strength in post-training, fine-tuning, or model-evaluation work. Relevant experience may include RL, SFT, LoRA/PEFT, full fine-tuning, RLHF, DPO, PPO, reward modeling, or training environments.

Strong Python skills and the 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: you can form hypotheses, choose meaningful metrics, diagnose failures, and distinguish signal from noise.

Experience designing systems—not only implementing specifications—including the ability to make tradeoffs around quality, scale, reliability, and reuse.