Talent Partner/Sourcer - Tech

What if the bottleneck to building frontier AI isn't compute or capital, but finding the researchers and engineers who can actually do the work?

We're the ~50-person team behind Stable Diffusion, Stable Video Diffusion, and FLUX.1—models with 400M+ downloads. But here's the challenge: we're competing for talent against companies with 10x our resources. ML researchers choosing between us, Google, and OpenAI. Computer vision engineers courted by every major tech company. Infrastructure specialists who could work anywhere. Your job is to find them, convince them, and close them—before someone else does.

You'll be our first dedicated Technical Talent Partner, owning full-cycle recruiting for the most specialized technical roles in AI. This isn't about filling requisitions—it's about building pipelines from scratch for roles where qualified candidates number in the dozens globally, then convincing those candidates that BFL is where they should do the best work of their careers.

You'll be the person who:

• Sources and attracts world-class technical talent by building robust pipelines for specialized roles—ML researchers, computer vision engineers, 3D specialists, training infrastructure, distributed systems—where traditional recruiting approaches fall short
• Excels at direct sourcing, knowing where to find top talent (academia, research labs, niche technical communities) and how to engage them effectively when they're not looking
• Develops creative strategies to reach passive candidates who aren't on LinkedIn and crafts compelling, personalized outreach that breaks through noise and generates response rates that exceed benchmarks
• Maps talent landscapes for emerging technical domains and proactively builds networks before roles open—because reactive recruiting loses in this market
• Owns end-to-end recruiting for technical roles across the stack—research, applied ML, infrastructure, and product engineering—moving fast without compromising quality
• Assesses technical depth through effective screening conversations and structured evaluation frameworks, knowing when candidates have the skills versus when they just have the keywords
• Proactively identifies and eliminates recruiting bottlenecks to maintain hiring velocity in a high-growth environment
• Builds deep trust with engineering and research leaders by understanding their technical requirements and team dynamics, not just their job descriptions
• Coaches managers on effective interviewing, technical assessment design, and closing strategies—pushing back constructively when needed because you're a partner, not an order-taker
• Develops sophisticated closing strategies for talent choosing between FAANG offers, startup equity, and research positions—serving as a compelling ambassador for BFL's research pedigree and technical mission
• Creates high-touch, personalized candidate experiences that differentiate us from larger tech companies with bigger budgets
• Navigates complex negotiations involving equity, research freedom, and publication policies in ways that close competitive deals

• How do you find ML researchers with diffusion model expertise when there are maybe 100 qualified people globally?
• What makes a passive candidate at Google or OpenAI actually respond to outreach from a 50-person startup?
• How do you assess technical depth for specialized roles when you're not a domain expert yourself?
• Where's the line between moving fast and compromising quality in technical hiring?
• How do you build pipelines for roles that didn't exist two years ago and don't have established talent pools?
• What closing strategies actually work when candidates have multiple FAANG offers and academic positions on the table?
• How do you maintain hiring velocity with limited resources while competing against recruiting teams 10x your size?

These aren't hypothetical—they're challenges you'll navigate daily in the most competitive talent market in tech.

You're a sourcing-led recruiter who gets energized (not drained) by finding hard-to-reach talent. You've built pipelines from scratch for specialized technical roles and developed a reputation for closing competitive hires. You can read a GitHub profile, understand ML research domains, and hold credible conversations with engineers and researchers without pretending to be one.

You likely have:

• 5+ years of full-cycle recruiting experience with deep expertise recruiting technical talent—software engineers, ML engineers, researchers, infrastructure engineers
• Exceptional sourcing skills with a proven track record of building pipelines from scratch for hard-to-fill technical roles
• Experience recruiting for AI/ML, deep tech, or research-driven companies where the talent bar is exceptionally high
• Strong execution skills—you ship results, not excuses, and know when to be scrappy versus methodical
• Experience recruiting across US and European markets with understanding of geographical nuances, visa processes, and relocation logistics
• Technical fluency to engage credibly with engineers and researchers—you can discuss technical concepts, evaluate GitHub profiles, and understand research papers at a high level
• A reputation for building trust and credibility quickly with technical hiring managers and candidates
• Outstanding written communication skills, especially for crafting compelling outreach that generates responses
• Deep familiarity with ATS platforms and modern sourcing tools (LinkedIn Recruiter, GitHub, Boolean search, etc.)

We'd be especially excited if you:

• Were a first or early talent hire at a scaling AI/ML or deep tech startup and built the recruiting function from scratch
• Have a track record recruiting research scientists or engineers with specialized ML backgrounds (computer vision, diffusion models, 3D, HPC, distributed training)
• Bring experience sourcing from academic/research communities and navigating industry-academia transitions
• Are comfortable with ambiguity and rapidly changing technical requirements
• Have deep knowledge of European technical talent markets
• Have built technical candidate pipelines through conferences, open source communities, and research networks

You love the hunt—sourcing energizes you, and you're relentless about finding the right person even when they're hard to reach. You speak tech well enough to read GitHub profiles and understand ML research domains. You thrive on velocity, moving quickly and iterating without getting paralyzed by imperfect information. You own outcomes completely, taking full accountability and doing whatever it takes to deliver results. You build relationships fast because you're credible, responsive, and you deliver. You stay organized under pressure, managing high-volume sourcing campaigns and competing priorities without dropping balls. You bring both strategic thinking and tactical execution—zooming out to advise on hiring strategy and zooming in to write compelling Boolean searches. You're excited to establish recruiting foundations at a research-driven AI company competing in the most talent-competitive sector in tech.

We're not just filling roles—we're winning talent wars against companies with 10x our resources. Every hire you close strengthens our competitive position. Every pipeline you build gives us access to talent others can't reach. Every relationship you nurture becomes a future hire or referral source. If that sounds more compelling than maintaining existing recruiting operations, we should talk.

Base Annual Salary: $155,000–$200,000 USD

We're based in Europe and value depth over noise, collaboration over hero culture, and honest technical conversations over hype. Our models have been downloaded hundreds of millions of times, but we're still a ~50-person team learning what's possible at the edge of generative AI.