# Product Engineer - Parse at Firecrawl

> Build and ship the document-parsing endpoint that turns PDFs, Word docs and spreadsheets into LLM-ready data.

- Company: Firecrawl (firecrawl.dev)
- Location: San Francisco HQ; Toronto Hub (remote)
- Places: San Francisco HQ, Toronto Hub, Remote
- Posted: 2026-08-28
- Work mode: Hybrid
- Type: Full-time
- Pay: $235,000–$260,000 USD/year (SF) / $217,000–$233,000 CAD/year (Toronto)
- Apply: https://jobs.ashbyhq.com/firecrawl/4fed8a26-d8e1-47b1-9283-59e547e6e02a
- Page: https://ye.saranatour.dev/jobs/product-engineer-parse-firecrawl-kn74krqdxm7zw7y570tbrdg5098hhbsw

## The posting

Product Engineer - Parse
Firecrawl · San Francisco HQ; Toronto Hub

# **Product Engineer - Parse**

You'll build the endpoint developers reach for when they need to turn documents (PDFs, Word docs, spreadsheets, and more) into clean, layout-aware data an AI can actually use. Parse takes a local or non-public file and returns LLM-ready markdown or structured JSON in a single call, with tables, headings, footnotes, and reading order preserved. This is hands-on product engineering at the core of what Firecrawl does: shipping the document-parsing features that thousands of developers hit every day, owning them from idea to production, and obsessing over the details that make an API a joy to build on. You'll ship real product from day one, not tickets in a backlog.

**Salary Range:** $235,000–$260,000 USD/year (SF) / $217,000–$233,000 CAD/year (Toronto)

**Equity Range:** Competitive equity. Details shared during the process.

**Location:**San Francisco, CA (SF HQ) or Toronto, ON (Toronto Hub). Hybrid, onsite 3+ days a week.

**Job Type:** Full-Time

**Experience:** 3+ years building and shipping developer-facing product

**Work Authorization:** Open to candidates already authorized to work in the US or Canada. In the US, we also support H-1B transfers, F-1 OPT and STEM OPT, TN (Canada and Mexico), and E-3 (Australia). We’re not able to sponsor new H-1B visas for candidates outside the US. For Canada, we’ll consider work permit sponsorship case by case through our Toronto Hub.

# **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. It's the boring-hard problem everyone building with LLMs eventually hits, solved.

In September 2026, we raised a $75M Series B led by Smash Capital, and we're spending it building the largest repository of knowledge in the world. We hit 8 figures in ARR in year one and more than doubled it in year two. We have 192k+ GitHub stars, putting us in the top 40 repositories of all time, 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. That library is called Alexandria, and it starts now.

# **Our Values**

- **Bias to action:** We move fast, ship hard, and don’t wait to be told.
- **Realistic about AI:** We’re clear-eyed about what AI can and can’t do, and we keep pushing to use it where it actually works.
- **All in:** The next few years matter a lot, and we’re all in for them.
- **Human connection:** As intelligence gets cheaper, relationships with customers, partners, and each other matter more.

# **What You'll Do**

- Build and ship developer-facing document-parsing features from idea to production
- Own the reliability and quality of parsed output across PDFs, Word docs, spreadsheets, and more, including the messy scanned and mixed-format files that break traditional parsers
- Turn hard, ambiguous problems (layout detection, table structure,