Interested in this Research Engineer role at Firecrawl?
Apply Now →About This Role
Research Engineer
=====================
You'll build the evaluation systems that tell us whether Firecrawl actually works. That sounds simple. It isn't. Our core promise, convert any URL into clean, structured, LLM\-ready data reliably, is hard to measure rigorously across millions of different websites, formats, and edge cases. As the systems we're measuring get more complex, the question "did that work?" gets harder, not easier.
This isn't an eval role where you inherit a framework and run benchmarks. You'll design the metrics, build the pipelines, generate the datasets, and own the feedback loop from output quality back to model and product decisions. If you care about what "good" actually means and have the engineering depth to measure it, this is the role.
Salary Range: $210,000–$275,000/year (Range shown is for U.S.\-based employees in San Francisco, CA. Compensation outside the U.S. is adjusted fairly based on your country's cost of living.)
Equity Range: Competitive equity — details shared during the process.
Location: San Francisco, CA (Hybrid, on\-site required)
Job Type: Full\-Time
Experience: 4\+ years in ML, research engineering, or data\-heavy backend, with real evaluation work
Visa: Must already be authorized to work in the US or our eligible remote\-hire regions. We're not able to sponsor visas right now, though that may change down the line.
About Firecrawl
===================
Firecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM\-ready markdown or structured data \- the boring\-hard problem everyone building with LLMs eventually hits, solved.
We hit 8 figures in ARR in year one and more than doubled it in year two. We have 147k\+ GitHub stars, and developers, agents, and category\-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.
We're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves \- no hiding behind process or headcount.
This is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web.
What You'll Do
==================
- Design the metrics that define what "good output" actually means across millions of sites, formats, and edge cases
- Build the pipelines and harnesses that measure quality rigorously and at scale
- Generate and curate the datasets that make evaluation trustworthy
- Own the feedback loop from output quality back to model and product decisions
- Turn "did that work?" into an answer the whole team can act on
What We're Looking For
==========================
- You have the engineering depth to build real evaluation systems, not just run existing ones
- You care deeply about what "good" means and how to measure it rigorously
- You're comfortable owning ambiguous problems where the metric itself has to be invented
- You move fast and close the loop \- you'd rather ship, measure, and iterate than perfect on paper
What We're NOT Looking For
==============================
- Someone who only wants to run benchmarks someone else designed
- A pure researcher who won't build the systems, or a pure engineer who won't think about methodology
- Someone who needs a fully\-specced ticket to start
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 — $210,000\-$275,000/year (U.S.\-based), based on impact, not tenure
- Own a piece — Gain competitive 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 all parents
- 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
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- 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 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
=====================
- Application Review — Send us your work and a quick note on why this excites you. Show us what you've built — eval systems, metrics you designed, datasets you created, quality problems you measured. We care about what you've shipped, not where you went to school.
- Intro Chat (\~25 min) — 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 Chat (\~45 min) — We'll dig into a real problem from our world — how you'd measure whether messy web output is actually "good," and build the system to prove it. Come ready to think out loud; we care how you reason, not whether you memorized the answer.
- Founder Chat (\~25 min) — Culture, pace, ownership, and how you like to work. Time for your questions too.
- Paid Work Trial (1\-2 weeks) — Work with the team on a real, scoped evaluation problem — paid at a contractor rate. It's the truest signal for both sides: you see what building at Firecrawl actually feels like, and we see how you ship. Remote\-friendly, and we'll flex around your current commitments.
- Decision — We move fast after the trial.
If you care about what "good" actually means and have the engineering depth to measure it, you should join us.
Apply now.
Compensation Range: $210K \- $275K
Salary Context
This $210K-$275K range is above the 75th percentile for Research Engineer roles in our dataset (median: $188K across 44 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 3,708 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Firecrawl, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills in Demand for This Role
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $280,000 based on 147 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($242K) sits 13% below the category median. Disclosed range: $210K to $275K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Firecrawl AI Hiring
Firecrawl has 1 open AI role right now. They're hiring across Research Engineer. Based in San Francisco, CA, US. Compensation range: $275K - $275K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national median.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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