Senior AI Forward Deployed Engineer

ABOUT HANDSHAKE

Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions.

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

Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale.

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

Responsibilities

• 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

Requirements

• 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.)