Research Engineer (Focused on RL)

Research Engineer (Focused on RL)

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. If you care as much about training throughput as you do about reward design, this is the role.

Salary Range: $180,000–$270,000/year (Range shown is for U.S.-based employees. Compensation outside the U.S. is adjusted fairly based on your country's cost of living. You can explore how we calculate this here: https://www.firecrawl.dev/careers/compensation.)

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

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 90k+ 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 super-intelligence 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. You'll own the full loop — data collection, reward modeling, training runs, evaluation, and deployment. You build the infra yourself because you're the one who needs it to work.

• Fine-tune models to achieve state-of-the-art results: Take foundation models and make them dramatically better at web data extraction, content understanding, and structured output generation. You know how to get from "decent fine-tune" to "best-in-class" and you have the patience and rigor to close that gap.

• Bridge LLM agents and classical RL: The most interesting problems at Firecrawl sit at the intersection of modern LLM-based agents and classical RL techniques. You'll design reward signals for agent behaviors, apply RL methods to improve multi-step agent workflows, and figure out where traditional RL approaches outperform prompting — and vice versa.

• Run fast experiments and iterate: You design experiments that test meaningful hypotheses, run them quickly, and make decisions based on results. You don't spend weeks on experiment infrastructure before getting a single result. Speed of iteration is a core part of how you work.

• Communicate clearly to non-RL people: RL can be opaque. You translate your work into language that engineers, product people, and leadership can understand and act on. You know how to explain why a reward function matters without requiring everyone to read the paper.

• Collaborate across the research team: Work closely with the Head of Research and the Search/IR-focused Research Engineer to connect RL improvements with search, ranking, and the broader product strategy.

What We're Looking For

Someone who builds their own training infra and reward pipelines. You don't wait for an ML platform team to set things up. You build the training loops, reward models, data pipelines, and evaluation frameworks yourself — because you understand that the infra choices directly affect the quality of the results. You've operated GPU clusters, managed training runs, and debugged convergence issues in production.

Can fine-tune models to achieve SOTA results. You've taken models from baseline to best-in-class on tasks that matter. You understand the full fine-tuning lifecycle — data curation, training dynamics, hyperparameter sensitivity, evaluation methodology — and you have the taste to know when a model is actually good versus when the eval is flattering.

Bridges LLM agents and classical RL approaches. You're fluent in both worlds. You understand PPO, RLHF, reward modeling, and policy optimization — and you also understand how modern LLM agents work, where they fail, and how RL techniques can make them better. You see connections between these domains that most people miss.

Runs fast experiments and communicates clearly. You bias toward quick iterations over perfect setups. You'd rather run three rough experiments this week than one polished one next month. And when you have results, you communicate them clearly — to other researchers, to engineers, and to leadership. No one needs to decode your work to understand its impact.

Production-minded. You care about whether your models actually work in production, not just on benchmarks. You've deployed models that serve real traffic and you've made hard tradeoffs between model quality, latency, and cost. Research that doesn't ship isn't research that matters here.

Backgrounds that tend to do well: RL engineers at AI labs or applied ML teams who've shipped models to production. Researchers who've done RLHF or reward modeling for LLM systems. ML engineers who've built training infrastructure at startups and cared as much about the pipeline as the model. People who've worked at the intersection of RL and language models — whether in academic labs with a production bent or at companies building agent systems.

What We're NOT Looking For

Pure theorists. If your best RL work is a proof in a paper and you've never trained a model on real data at real scale, this isn't the role. We need someone who builds and ships, not someone who derives.

Researchers who need a platform team. If you expect training infrastructure, data pipelines, and evaluation frameworks to be set up for you before you can be productive, you'll be frustrated here. You build the tools you need.

People who only know one paradigm. If you're deep in classical RL but have never worked with LLMs, or if you're an LLM fine-tuner who's never touched RL — you'll be missing half the picture. This role requires fluency in both.

Slow iterators. If your standard experiment cycle is measured in weeks, not days, you'll struggle with the pace here. We need someone who can run a meaningful experiment, interpret results, and decide next steps within a day or two — not someone who needs a month-long study to make a call.

Black-box communicators. If your typical update is a wall of metrics that only another RL researcher can interpret, this isn't the right fit. We need someone who can explain what's working, what's not, and why it matters to people who don't have RL PhDs.

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

Available to all employees

• Salary that makes sense — $180,000–$270,000/year, based on impact, not tenure

• Own a piece — Up to 0.15% equity in what you're helping build

• Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge

• Parental leave — 12 weeks fully paid, for moms and dads

• Wellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you human

• Learning & Development — Expense up to $1,000/year toward anything that helps you grow professionally

• Team offsites — A change of scenery, minus the trust falls

• Sabbatical — 3 paid months off after 4 years, do something fun and new

Available to US-based full-time employees

• Full coverage, no red tape — Medical, dental, and vision (100% for employees, 50% for spouse/kids) — no weird loopholes, just care that works

• Life & Disability insurance — Employer-paid short-term disability, long-term disability, and life insurance — coverage for life's curveballs

• Supplemental options — Optional accident, critical illness, hospital indemnity, and voluntary life insurance for extra peace of mind

• Doctegrity telehealth — Talk to a doctor from your couch

• 401(k) plan — Retirement might be a ways off, but future-you will thank you

• Pre-tax benefits — Access to FSAs and commuter benefits (US-only) to help your wallet out a bit

• Pet insurance — Because fur babies are family too

Available to SF-based employees

• SF HQ perks — Snacks, drinks, team lunches, intense ping pong, and peak startup energy

• E-Bike transportation — A loaner electric bike to get you around the city, on us

Interview Process

1. Application Review — Send us your stuff + 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.

2. Technical Deep Dive (~60 min) — Go deep on RL and model training work you've done: training infrastructure decisions, reward design, fine-tuning approaches, and production deployment. We'll explore a live problem — how you'd approach applying RL to improve an LLM agent workflow at Firecrawl. We're looking for depth across both classical RL and modern LLM techniques, production instincts, and fast reasoning.

3. Founder Chat (~30 min) — Culture, pace, ownership, and how you like to work. Time for your questions too.

4. Paid Work Trial (1–2 weeks) — Test drive the real thing: tackle a real RL/fine-tuning problem with production implications. We'll evaluate on technical depth, experiment velocity, and how clearly you communicate results.

5. 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 better at understanding and extracting web data at scale — this is your shot.

👉 Apply now and let's train something great. 🧠