Senior AI Forward Deployed Engineer

Handshake
San Francisco, CA, United States
about 1 month ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
6 years minimum
Compensation
$257,000.0 - $300,000.0
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Python (Programming Language) Machine Learning Open Source Technology Reinforcement Learning Data Processing Large Language Models Model Validation Data Pipelines

Job description

For cash compensation, we set standard ranges for all U.S.-based roles based on function, level, and geographic location, benchmarked against similar stage growth companies. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. Final offer amounts are determined by multiple factors, including geographic location as well as candidate experience and expertise, and may vary from the amounts listed above., 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., * 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.). 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

Benefits & conditions

3.83.8 out of 5 stars San Francisco, CA Hybrid work $257,000 - $300,000 a year - Full-time, Pulled from the full job description

  • Referral program
  • Paid parental leave
  • Food provided
  • Parental leave
  • Health insurance
  • 401(k) matching
  • Paid time off, * $257K - $300K, 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 Compensation Range: $257K - $300K

About the company

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.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.indeed.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

1:31 min

Essential AI and human skills for future teams

Alexander Weißhaupt Alexander Weißhaupt +1 · WWC 2025

6:08 min

Applying software engineering environments and testing to data pipelines

Matthias Niehoff Matthias Niehoff ¡ WWC 2024

2:10 min

Defining stream data processing versus standard event processing

Soroosh Khodami Soroosh Khodami ¡ WWC 2024

2:36 min

Applying supervised machine learning for practical rule extraction

Katja Träumner

2:14 min

Exploring internal AI product initiatives and global engineering roles

Maria Apazoglou ¡ Coffee With Developers

47 sec

Building modern data pipelines for legacy exports

Dr. Alexander Wachtel Dr. Alexander Wachtel +1 ¡ WWC 2025

Videos

See all

Related articles

See all