Senior AI Forward Deployed Engineer

Handshake•San Francisco, CA

4d•$250,000 - $290,000•Hybrid

About The Position

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.

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.). 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

Nice To Haves

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

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