Senior AI Forward Deployed Engineer

Handshake; San Francisco (CA); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Equity in a fast-growing company

401(k) match

Paid parental leave

Medical, dental, and vision healthcare

$2,000 learning stipend

Job summary

Handshake is seeking a Senior Forward Deployed AI Engineer to lead high-impact research engagements at the forefront of AI technology. This role involves partnering with AI labs to translate research goals into practical projects and mentoring a team of engineers.

The ideal candidate should have over 6 years of experience in applied ML, strong Python skills, and a solid background in reinforcement learning. This position offers a hybrid work model in San Francisco.

Qualifications

• 6+ years in applied ML or closely related field with model training experience.
• Expertise in Python and ML stack from data processing to model evaluation.
• Solid understanding of reinforcement learning and post-training techniques.

Responsibilities

• Partner with AI lab researchers to translate research questions into projects.
• Design and deliver evaluation frameworks and benchmark infrastructure.
• Mentor other engineers, establishing technical standards for AI work.

Skills

Applied ML

Python skills

Reinforcement learning knowledge

Experience with ML data pipelines

Stakeholder management

Tools

Data labeling systems

Eval frameworks

Job description

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

• Equity in a fast‑growing company
• 401(k) match, competitive compensation, financial coaching
• Paid parental leave, fertility benefits, parental coaching
• Medical, dental, and vision, mental health support, $500 wellness stipend
• $2,000 learning stipend, ongoing development
• Commuting support, free lunch, and gym in our SF office
• Flexible PTO, 15 holidays + 2 flex days
• Team outings & referral bonuses