Research Engineer – Evals

Firecrawl; San Francisco (CA); On-site; USD 160,000 - 240,000; Full time

Benefits offered by this job

Unlimited PTO

12 weeks fully paid parental leave

Wellness stipend

Learning & Development budget

3 paid months sabbatical after 4 years

Medical, dental, and vision insurance

401(k) plan

Pet insurance

Job summary

Firecrawl is seeking a Research Engineer for Evals to build the evaluation systems that ensure the quality of outputs. This role involves designing metrics, owning feedback loops, and developing robust evaluation frameworks. The candidate should have a strong ML engineering background with at least 3 years of experience and a firm grasp on data quality. The role offers a salary range of $160,000 to $240,000 and allows for remote work across the Americas, contributing to a dynamic and fast-paced technological environment.

Qualifications

• 3+ years of experience in ML engineering, applied AI, or data quality.
• Proven experience building evaluation frameworks.
• Strong background in working with unstructured web data.

Responsibilities

• Build the evaluation stack from scratch to measure output quality.
• Design benchmarks reflecting real unstructured web data.
• Own automated evaluation systems for LLM output.

Skills

ML engineering

Data quality

Pipeline design

Metric definition

Labeled dataset curation

Job description

Research Engineer — Evals

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 we layer in models and agent workflows, 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: $160,000 to $240,000/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: Up to 0.10%

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

Job Type: Full‑Time

Experience: 3+ years in ML engineering, applied AI, or data quality — 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 the eval stack from scratch. Design and own the systems that measure whether Firecrawl’s outputs are actually good — across scrape, crawl, extract, and map. That means defining metrics, building pipelines, curating datasets, and integrating evals into CI/CD so regressions get caught before they ship. You build the infra yourself because you’re the one who needs it to work.

Design benchmarks that reflect reality. Our outputs need to hold up across millions of websites — SPAs, paywalled content, dynamic rendering, structured and unstructured formats. You’ll build benchmark datasets that cover the real distribution of what our customers send us, including the edge cases that break naive approaches. Ground truth doesn’t come for free — you’ll design the collection and labeling systems too.

Own LLM‑as‑judge pipelines. You’ll design and validate automated judges that score extraction quality at scale, know the failure modes of LLM‑based evaluation, and build the human review tooling needed when automation isn’t enough. You understand the difference between an eval that measures something real and one that just flatters the system.

Close the loop with models and RL. Evals here aren’t a reporting layer — they’re a training signal. You’ll work closely with the RL and Search/IR research engineers to turn quality measurements into reward signals and feedback loops that make models meaningfully better. Your benchmarks directly influence what gets trained next.

Run fast experiments and communicate clearly. You design experiments that test meaningful hypotheses, run them quickly, and make decisions based on results. When you have findings, anyone on the team can understand what they mean — no decoder ring required.

What We’re Looking For

Builds their own eval infrastructure. You don’t wait for tooling to appear. You write the pipelines, curate the datasets, design the rubrics, and validate the judges yourself — because you understand that infra choices directly affect what you’re actually measuring. You’ve run evals at scale and debugged the places where they lie.

Knows what “good” means for unstructured web data. You’ve worked with messy, real‑world data before. You understand why markdown quality is hard to define, why structured extraction fidelity varies by schema, and why naive string‑match metrics miss the point. You have strong opinions about what a useful benchmark actually looks like — and the rigor to validate them.

Fluent in LLM evaluation methodology. You understand LLM‑as‑judge systems, their correlation with human judgment, and where they break down. You’ve designed rubrics that hold up under adversarial inputs, built human review pipelines that scale, and know how to measure inter‑rater agreement. You’re not fooled by evals that only look good in aggregate.

Production‑minded. You care about whether your evals reflect real production behavior, not just offline benchmarks. You’ve worked on systems serving real traffic and made hard tradeoffs between evaluation depth, coverage, and cost. A benchmark that doesn’t represent what customers actually send isn’t a benchmark worth maintaining.

Fast and clear. You’d rather run three rough experiments this week than one polished one next month. When you have results, anyone on the team can understand what they mean — and what to do next.

Backgrounds that tend to do well: ML engineers who’ve built eval or data quality systems at AI labs or applied teams. Engineers who’ve worked on LLM fine‑tuning or RLHF pipelines and understand how feedback quality drives model improvement. People who’ve worked at the intersection of data infrastructure and model development. Anyone who’s been the person on the team asking “but how do we know this actually works?”

What We’re NOT Looking For

Benchmark runners. If your eval experience is running existing frameworks on existing benchmarks and reporting numbers, this isn’t the right fit. We need someone who builds the frameworks and defines the benchmarks.

People who treat evals as an afterthought. If your default workflow is to build first and evaluate later — or to treat pass rates as a proxy for actual quality — you’ll struggle here. Evals are a first‑class product, not a QA gate.

Researchers who need a platform team. If you expect pipelines, datasets, and labeling infrastructure to exist before you can be productive, you’ll be frustrated. You build the tools you need.

Slow iterators. If your standard experiment cycle is measured in weeks, not days, you’ll struggle with the pace. We need someone who can design, run, and interpret a meaningful experiment within a day or two.

Bonus Points

• Any other niche expertise and skills
• Previous experience at a scraping, automation, or security‑focused startup
• Ex‑founder

Benefits & Perks

Available to all employees

• Salary that makes sense — $160,000-240,000/year (U.S.-based), based on impact, not tenure
• Own a piece — Up to 0.1% equity in what you’re helping build
• Unlimited PTO — Minimum 3 weeks off encouraged; 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 $150/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
• 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, and the occasional burst of chaotic startup energy