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
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 RoleAs 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 officeWhat 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
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
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
Handshake delivers benefits that help you feel supported—and thrive at work and in life.
The below benefits are for full-time US employees.
🎯 Ownership: Equity in a fast-growing company
💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching
🍼 Family Support: Paid parental leave, fertility benefits, parental coaching
💝 Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend
📚 Growth: $2,000 learning stipend, ongoing development
💻 Office: Commuting support, free lunch, and gym in our SF office
🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days
🤝 Connection: Team outings & referral bonuses
Skills Required
- 6+ years of experience in applied ML, AI research engineering, or closely related field with exposure to model training workflows and post-training techniques
- Strong Python skills
- 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.)
- Hands-on experience fine-tuning or lightweight optimization of ML models (Tinker, LoRA, PEFT, or similar)
- Experience with ML data pipelines and tooling (data labeling systems, eval frameworks, quality metrics)
- Excellent communication and stakeholder management; ability to translate between researcher intuition and engineering reality
- Strong prioritization instincts and ability to triage multiple urgent customer needs
- Track record of leading technical projects end-to-end in ambiguous, fast-moving environments
- Experience with evaluation design for LLMs or RLHF pipelines in production customer environments
- Published research, benchmarking work, or contributions to open-source AI/ML tooling
- Prior forward-deployed, solutions engineering, or technical consulting experience at a high-growth AI company
- Familiarity with annotation platform tooling, quality control frameworks, or human feedback collection at scale
Handshake Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Handshake and has not been reviewed or approved by Handshake.
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Leave & Time Off Breadth — Time-off practices include flexible/unlimited PTO, companywide recharge weeks in summer and winter, plus additional volunteer and personal holiday time. Feedback suggests sabbaticals and coordinated breaks help people actually use rest time.
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Parental & Family Support — Parental leave is described as extended for primary and secondary caregivers, and family-oriented policies are highlighted. Fertility and family-support resources are referenced in public materials.
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Healthcare Strength — Core coverage spans medical, dental, and vision, with added mental-health resources and wellness programming. Feedback suggests these supports contribute meaningfully to overall wellbeing.
Handshake Insights
What We Do
Handshake is the #1 place to launch a career with no connections, experience, or luck required. The platform connects up-and-coming talent with 650,000+ employers - from Fortune 500 companies like Google, Nike, and Target to thousands of public school districts, healthcare systems, and nonprofits. Earlier this year, we announced our $200M Series F funding round. This Series F fundraise and new valuation of $3.5B will fuel Handshake’s next phase of growth and propel our mission to help more people start, restart, and jumpstart their careers.
Why Work With Us
How someone builds their career is foundational. We believe in working with the higher education community to help students build meaningful careers. We are at the nexus of universities, students and employers— and we’re able to connect the very best pieces of each side. We’re proud to create a community where students can be more successful.
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