{"id":68988,"url":"https://alion.io/job/firecrawl-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":2391,"name":"Firecrawl","domain":"firecrawl.dev","url":"https://alion.io/company/firecrawl","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":83,"open_postings":8,"ghost_share":0,"stale_share":0.875,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":250000,"max":290000,"currency":"USD","period":"year","gross":null,"usd_annual":290000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Firecrawl","optional":false},{"name":"GitHub","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Feature Store","optional":true},{"name":"Kubernetes","optional":true},{"name":"MLFlow","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-08-20T21:40:10Z","employer_posted_date":"2026-08-20","last_verified_at":"2026-10-16T20:31:13Z","board_verified":true,"closed_at":null,"days_open":57,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":57},"description":"Machine Learning Engineer\nYou'll build the ML behind Firecrawl: the models and the systems that serve them. That starts with search: training and shipping the ranking and relevance models for one of our fastest-growing products, then extending that work across extraction quality and LLM-driven features. You'll also own how we measure: A/B testing launches and building the experimentation frameworks the whole team ships against. If you ship models into production, whether your title says ML engineer or data scientist, this is for you.\nSalary Range: $250,000-$290,000 USD/year (SF) / $210,000-$224,000 CAD/year (Toronto)\nEquity Range: Competitive equity. Details shared during the process.\nLocation: San Francisco, CA (SF HQ) or Toronto, ON (Toronto Hub). Hybrid, onsite 3+ days a week.\nJob Type: Full-Time\nExperience: 3+ years building ML or data-heavy systems in production\nWork 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.\nAbout Firecrawl\nFirecrawl 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.\nIn 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 187k+ 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.\nWe'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.\nThis 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.\nWhat You'll Do\nImprove ranking and relevance for Firecrawl Search, from feature engineering to model training to production\n\nBuild and tune models for learning-to-rank, query understanding, and LLM-driven retrieval\n\nExtend ML across Firecrawl's products: extraction quality, content classification, and evaluation of LLM-driven features\n\nMine query logs and behavioral data at scale to find where our products win and where they fail\n\nBuild the data pipelines that turn web-scale crawl and query data into training data and features\n\nWork hands-on with platform, search, and cloud DevOps engineers to get models running fast and cheap in production\n\nDesign our testing strategy: the A/B testing frameworks and offline evaluation the team ships against\n\nPartner on product launches across Firecrawl: define success metrics, run the experiments, and make the ship/no-ship call on evidence\n\nReport on how releases perform post-launch and turn the findings into the next iteration\n\nWhat We're Looking For\nYou've shipped ML models into production systems and owned them after launch: deploying, monitoring, and retraining them, not handing them off\n\nYou have real ranking or relevance-modeling experience: learning-to-rank, recommendations, or search quality\n\nYou're comfortable in large, data-heavy systems: query logs, pipelines, and datasets that don't fit in memory\n\nYou write production-quality code (Python at minimum) and can work inside a real backend codebase\n\nYou're rigorous about measurement. You've designed and analyzed A/B tests and know when a lift is real\n\nYou can communicate results clearly to the team: what shipped, what moved, and what to do next\n\nNice to Have\nMLOps experience: MLflow, experiment tracking, model registries, or feature stores. Kubernetes is a plus\n\nExperience building or standardizing an experimentation framework at a previous company\n\nExperience with embedding models, vector retrieval, or LLM-based relevance evaluation\n\nExperience evaluating LLM outputs at scale: quality scoring, structured-extraction accuracy, or agent behavior\n\nSpark or similar large-scale data processing experience\n\nWhat We're NOT Looking For\nA pure statistician or analyst who needs an engineering team to productionize their work\n\nSomeone who wants to specialize narrowly and hand off everything else\n\nSomeone who optimizes for process over shipping\n\nA Note On Pace\nWe 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.\nBenefits & Perks\nAvailable to all employees\nSalary that makes sense: $250,000-$290,000 USD/year (SF) / $210,000-$224,000 CAD/year (Toronto), based on impact, not tenure\n\nOwn a piece: Gain competitive equity in what you're helping build\n\nGenerous PTO: 15 days mandatory, anything after 24 days, just ask (holidays excluded). Take the time you need to recharge\n\nParental leave: 12 weeks fully paid, for all parents\n\nWellness stipend: $100 USD/month for the gym, therapy, massages, or whatever keeps you human\n\nLearning & Development: Expense up to $1,000 USD/year toward anything that helps you grow professionally\n\nTeam offsites: A change of scenery, minus the trust falls\n\nSabbatical: 3 paid months off after 4 years, do something fun and new\n\nAvailable to US-based full-time employees\nFull coverage, no red tape: Medical, dental, and vision (100% for employees, 50% for partner and kids). No weird loopholes, just care that works\n\nLife & Disability insurance: Employer-paid basic life and AD&D, short-term disability, and long-term disability. Coverage for life's curveballs\n\nVirtual care and a health guide: Teladoc for the couch doctor visit, plus Rightway to answer coverage questions and fight billing errors for you\n\nMental health: Talkspace, therapy and psychiatry on your schedule\n\nFertility and family building: Carrot, covering you and your partner\n\nEAP: Free confidential counseling, legal and financial consults, and online will prep through Guardian\n\n401(k) plan: Retirement might be a ways off, but future-you will thank you\n\nPre-tax benefits: HSA, FSA, and commuter benefits to help your wallet out a bit\n\nSupplemental options: Extra life and AD&D, accident, critical illness, hospital indemnity, plus pet, legal, and identity protection through MetLife\n\nAvailable to Canada-based full-time employees\nFull coverage, no red tape: Extended health, dental, and vision through Manulife (Diamond, the top tier), 100% employer-paid for you, your partner, and your kids\n\nLife & Disability insurance: Employer-paid life, AD&D, short-term disability, and long-term disability. Coverage for life's curveballs\n\nVirtual care: Dialogue Premium, so you can see a doctor or nurse from your couch, any hour\n\nMental health: Talkspace Elite, therapy and psychiatry on your schedule\n\nFertility and family building: Carrot, covering you and your partner\n\nRetirement: Group RRSP through Wealthsimple, so future-you can thank you\n\nAvailable to SF-based employees\nSF HQ perks: Snacks, drinks, team lunches, intense ping pong, and peak startup energy\n\nE-Bike transportation: A loaner electric bike to get you around the city, on us\n\nAvailable to Toronto-based employees\nToronto Hub perks: Snacks, drinks, team lunches, glass-walled views down University Avenue, and a home base steps from Union Station\n\nTransit, covered: A PRESTO card loaded for GO Transit, subway, and streetcar, plus station parking if you drive to the train. Winter-proof, on us\n\nInterview Process\nApplication Review: Send us your work and a quick note on why this excites you. Show us what you've built: ranking models, search systems, experimentation frameworks, pipelines that fed production models. We care about what you've shipped, not where you went to school.\nIntro 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.\nTechnical Chat (~45 min): We'll dig into a real problem from our world (improving ranking quality with noisy relevance signals, designing the A/B test for a product launch, building features from query logs) and talk through how you'd approach it. Come ready to think out loud. We care how you reason, not whether you memorized the answer.\nFounder Chat (~25 min): Culture, pace, ownership, and how you like to work. Time for your questions too.\nPaid Work Trial (~40 Hours): Work with the team on a real, scoped piece of the product, 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.\nDecision: We move fast after the trial.\nIf you want your models ranking results for the whole web, and to see the impact in production the same week, you should join us.\nApply now.","description_format":"text","description_chars":9445,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity","Parental leave"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"},{"name":"Canada","iso":"CA","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Data Providers & Datasets","AI Agents","AI Development Tools","Developer Tools"],"lifecycle":[{"event":"open","at":"2026-08-20T21:40:10Z"}],"visa":[],"liveness":{"score":18,"band":"cold","label":"Long shot","p_open":1,"p_active":0.501,"p_room":0.35,"age_days":56,"expected_fill_days":18,"reasons":["conf:2","velocity","win:tail"],"computed_at":"2026-10-16T05:45:00Z"},"pay":{"stated_usd_annual":290000,"is_top_pay":true},"html_url":"https://alion.io/job/firecrawl-machine-learning-engineer","json_url":"https://alion.io/job/firecrawl-machine-learning-engineer.json","meta":{"generated_at":"2026-10-16T22:59:05Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":4656,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2391},"rest":"https://alion.io/mcp/rest/get_company?id=2391"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ffirecrawl-machine-learning-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ffirecrawl-machine-learning-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ffirecrawl-machine-learning-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/firecrawl-machine-learning-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ffirecrawl-machine-learning-engineer"}]}