Research Engineer — Reinforcement Learning

About the role

You'll bring reinforcement learning to Firecrawl's core product — building the training infrastructure, reward pipelines, and fine-tuning systems that make our models meaningfully better at extracting, understanding, and structuring web data. This isn't theoretical RL research. You'll build your own training infra, run fast experiments, ship models to production, and bridge the gap between classical RL approaches and modern LLM agent systems.

Responsibilities

• Build training infrastructure and reward pipelines from scratch.
• Design and operate the systems that train and evaluate Firecrawl's models.
• Own the full loop — data collection, reward modeling, training runs, evaluation, and deployment.
• Fine-tune models to achieve state-of-the-art results.
• Bridge LLM agents and classical RL.
• Run fast experiments and iterate.
• Communicate clearly to non-RL people.
• Collaborate closely with the team.

Requirements

• 3+ years in applied RL, ML engineering, or model training — with production systems.

Qualifications

• Fluent in both classical RL and modern LLM techniques.
• Production-minded.
• Can fine-tune models to SOTA.
• Runs fast experiments and communicates clearly.

Skills

• Reinforcement Learning
• Model Training
• Data Collection
• Reward Modeling
• Training Infrastructure
• Experimentation
• Communication

Benefits

• Salary Range: $180,000–$290,000/year (U.S.-based employees)
• Equity Range: Up to 0.15%
• Location: San Francisco, CA or Remote (Americas, UTC-3 to UTC-10)

Pay

Salary Range: $180,000–$290,000/year (U.S.-based employees)

Schedule

Full-Time

Job Type

Full-Time

Experience

• 3+ years in applied RL, ML engineering, or model training — with production systems

Equity

• Up to 0.15%

Location

• San Francisco, CA or Remote (Americas, UTC-3 to UTC-10)

Job Type

• Full-Time

Experience

• 3+ years in applied RL, ML engineering, or model training — with production systems

Visa

• US Citizenship/Visa required for SF; N/A for Remote

About Firecrawl

Firecrawl is the easiest way to extract data from the web. Developers use us to reliably convert URLs into LLM-ready markdown or structured data with a single API call. In just a year, we've hit 8 figures in ARR and 100k+ GitHub stars by building the fastest way for developers to get LLM-ready data. We're a small, fast-moving, technical team building essential infrastructure superintelligence will use to gather data on the web. We ship fast and deep.

What You'll Do

• Build training infrastructure and reward pipelines from scratch.
• Design and operate the systems that train and evaluate Firecrawl's models.
• Own the full loop — data collection, reward modeling, training runs, evaluation, and deployment.
• Fine-tune models to achieve state-of-the-art results.
• Bridge LLM agents and classical RL.
• Run fast experiments and iterate.
• Communicate clearly to non-RL people.
• Collaborate closely with the team.

What We're Looking For

• Builds their own training infra and reward pipelines.
• Operates GPU clusters, manages training runs, and debugs convergence issues in production.
• Fine-tunes models to SOTA.
• Bridges LLM agents and classical RL.
• Runs fast experiments and communicates clearly.

What We're Not Looking For

• Pure theorists.
• Researchers who need a platform team.
• People who only know one paradigm.
• Slow iterators.
• Black-box communicators.

A Note On Pace

We operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.

Benefits & Perks

• Salary Range: $180,000–$290,000/year (U.S.-based employees)
• Equity Range: Up to 0.15%
• Location: San Francisco, CA or Remote (Americas, UTC-3 to UTC-10)
• Job Type: Full-Time
• Experience: 3+ years in applied RL, ML engineering, or model training — with production systems
• Visa: US Citizenship/Visa required for SF; N/A for Remote

Interview Process

• Application Review: Send us your work and a quick note on why this excites you. Show us what you've trained — models, reward systems, training pipelines. Published work is great; shipped production models are better.
• Intro Chat: A quick conversation to get to know each other before we go deep. We'll talk about what you've been working on, what drew you to Firecrawl, and what you're looking for in your next role. Time for your questions too.
• Technical Deep Dive: Go deep on RL and model training work you've done: training infrastructure decisions, reward design, fine-tuning approaches, production deployment. We'll explore a live problem — how you'd apply RL to improve an LLM agent workflow at Firecrawl. We're looking for depth across classical RL and modern LLM techniques, production instincts, and fast reasoning.
• Founder Chat: Culture, pace, ownership, and how you like to work. Time for your questions too.
• Paid Work Trial: Tackle a real RL/fine-tuning problem with production implications. We evaluate on technical depth, experiment velocity, and how clearly you communicate results.
• Decision: We move fast after the trial. If you want to bring RL to one of the most interesting applied problems in AI — making agents smarter at understanding and extracting web data at scale — this is your shot.