Senior AI Forward Deployed Engineer at Handshake — San Francisco, CA

About Handshake

In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We've grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month.

Why join Handshake now:

• Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel
• Partner hand-in-hand with world-class AI labs, Fortune 500 partners and the world's top educational institutions
• Work together with engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders
• Build a massive, fast-growing business with billions in revenue

About Handshake AI

About the Role

As a Senior Forward Deployed AI Engineer, you'll sit at the intersection of applied AI research and customer delivery embedded with our most strategic partners, including leading frontier AI labs. You think like a researcher and ship like an engineer. You speak the language of the labs. You default to action and figure things out in motion.

You'll own the full lifecycle of high-impact research engagements from translating ambiguous lab requirements into concrete evaluation frameworks to prototyping pipelines and tooling that make them run. You'll make fast decisions, lead prioritization decisions, mentor engineers, and establish the patterns and systems that others follow. Your technical credibility with researcher audiences and your ability to move quickly in shifting environments are what set you apart.

This is a rare role: deep AI knowledge, real customer ownership, and the chance to influence how frontier models get trained.

Location: San Francisco, CA | Hybrid, 3x a week in office

What you'll do

• Partner directly with AI lab researchers to understand their post-training goals and data requirements, translating ambiguous research questions into scoped, executable projects
• Design and deliver evaluation frameworks, annotation pipelines, and benchmark infrastructure tailored to each lab's training methodology
• Prototype and iterate fast: stand up lightweight experiments, run evals, and interpret results in tight feedback loops with research partners
• Make key design decisions around data quality and evaluation design that hold up at scale
• Mentor and uplevel other engineers and researchers on the team, establishing technical standards for forward-deployed AI work
• Identify and document repeatable patterns across lab engagements to accelerate future deployments
• Stay current on the frontier: follow developments in RL, post-training, and benchmarking to bring relevant insight into every customer conversation

What we're looking for

• 6+ years of experience in applied ML, AI research engineering, or a closely related field with real exposure to model training workflows and post-training techniques
• Strong Python skills and comfort working across the ML stack: data processing, model evaluation, experiment tracking, pipeline tooling
• Solid working knowledge of reinforcement learning and post-training concepts (RLHF, DPO, PPO, etc.). You don't need to have trained frontier models, but you need to hold your own in a room of people who have
• Hands-on experience fine-tuning or lightweight optimization of ML models (Tinker, LoRA, PEFT, or similar). You've actually tinkered with models, not just read about it
• Experience with ML data pipelines and the tooling around them (e.g., data labeling systems, eval frameworks, quality metrics)
• Excellent communication and stakeholder management. You’re an apt translator between researcher intuition and engineering reality, and build trust with both
• Strong prioritization instincts: you know how to triage across multiple urgent customer needs and guide your team toward the highest-leverage work
• Track record of leading technical projects end-to-end in ambiguous, fast-moving environments

Extra Credit:

• Experience with evaluation design for LLMs or RLHF pipelines in production customer environments
• Published research or benchmarking work, or contributions to open-source AI/ML tooling
• Prior experience in a forward-deployed, solutions engineering, or technical consulting role at a high-growth AI company
• Familiarity with annotation platform tooling, quality control frameworks, or human feedback collection at scale

Perks

💻 Office: Commuting support, free lunch, and gym in our SF office