Head of Research

Head Of ResearchYou'll build and lead the research function at Firecrawl the team responsible for making our core technology meaningfully better than anything else on the market. That means search, ranking, information retrieval, fine-tuning, and model training applied to the hardest problems in web data extraction. This isn't a research lab. Everything you build ships to production and runs at scale. You'll start by doing the work yourself, then hire a small team of people who are smarter than you. Own the research roadmap: Define what Firecrawl needs to build to stay technically ahead across search, ranking, relevance, information retrieval, fine-tuning, and model training. You'll decide where we invest research effort and what makes it into production. Ship research to production at scale: Every project you lead ends with code running in prod, serving real customers at real volume. You'll work closely with the engineering team to make sure research output is production-grade fast, reliable, and maintainable. Go deep on search and IR: Build and improve the systems that power how Firecrawl finds, ranks, indexes, and retrieves web content. This is core to the product and core to the moat. You'll own freshness, dedup, query understanding, and incremental indexing at a scale that matters. Fine-tune and train models: Build and operate training pipelines that improve Firecrawl's extraction quality, content understanding, and data structuring. You know how to get from raw data to a model that moves a metric in production. Build and lead a high-output research team: You'll start hands-on, then hire 24 researchers who are deeply technical and ship fast. You know how to find and attract people who are smarter than you and how to create an environment where they do their best work. Collaborate across engineering and product: Research doesn't happen in a vacuum. You'll work with Nick and the engineering team to prioritize research investments that directly improve the product, and with the growth team to understand what customers need. Someone who has shipped research to production at scale. You've taken research from idea to experiment to deployed system not once, but repeatedly. You know the difference between a promising result in a notebook and a system that works at production volume with real-world data. You optimize for impact, not publications. Deep knowledge across search, IR, fine-tuning, and model training. You don't just know one corner of ML. You've built search indexes, trained ranking models, fine-tuned LLMs, and operated the infrastructure that supports all of it. You can go deep on any of these and you know how they connect. Experience building and leading small, high-output research teams. You've built research teams at startups ideally Series A through Series D, in the 10-to-50-person range. You know how to hire well, set direction without micromanaging, and create a culture where great researchers want to stay. Small team, big output is your default mode. Can recruit people smarter than them. This is non-negotiable. You have a network of strong researchers and you know how to sell a mission. You're not threatened by talent you're energized by it. The team you build will define Firecrawl's technical ceiling. Production-minded. You care about latency, cost, reliability, and maintainability as much as model quality. You've debugged production ML systems at 3am and you've made hard tradeoffs between accuracy and speed. You know that research that doesn't ship is research that doesn't matter. Backgrounds that tend to do well: Applied research leads at search companies, data infrastructure startups, or AI labs with a production focus. ML engineering managers who built search/ranking/IR systems at scale. Technical founders who built and shipped ML-powered products. Senior researchers who got frustrated with academia and wanted to ship. Pure academics. If your best work is a citation count and you've never shipped a model to production, this isn't the role. We respect the research we just need it to run in prod. Big-company research managers. If you led a 30-person team at Google Brain and your job was writing strategy docs and attending reviews, you'll be frustrated here. This role is hands-on for the first 6+ months. You write code, you train models, you debug pipelines. ML generalists who haven't gone deep on search/IR. We need specific depth in information retrieval, ranking, indexing, and search infrastructure. If your experience is mostly in computer vision, NLP classification, or recommendation systems without search/IR, this isn't the right fit. People who need perfect data and clean infrastructure to do good work. The data is messy. The infrastructure is evolving. If you need everything set up before you can be productive, you'll stall here. We need someone who builds the infrastructure as they go. 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.