Production & Shipping

• Ship models to production with our engineering team. Our ML stack is FastAPI, PostgreSQL, BigQuery, AWS App Runner, with retrieval via FAISS and sentence-transformers, and managed LLM APIs (Claude, Gemini)
• Build evaluation harnesses and monitoring so we know when models drift
• Keep latency budgets honest

LLM & Agent Systems

• Develop LLM-powered features across HostCopilot (drip campaigns, retention nudges, pricing and content suggestions) and Pal-facing surfaces (AI Concierge, semantic search, recommendations)
• Build agentic systems with tool use, RAG, structured outputs, evaluation loops, and human-in-the-loop where needed
• Decide when to prompt-engineer, when to fine-tune, and when a classical model is the better answer

Cross-Functional Impact

• Partner with product to size opportunities and translate findings into roadmap decisions
• Partner with growth and our data analyst to measure marketplace impact rigorously
• Set the bar for the squad on ML rigor: offline evaluation, experiment design, and writeups

What We're Looking For

Experience

• 5+ years of applied ML experience shipping models to production. Bonus if some of that was in marketplaces, search, or recommendations
• Track record of taking a problem from "vague PM ask" to "shipped feature that moved a metric"
• Comfort with the full lifecycle: framing, data, modeling, evaluation, deployment, monitoring

Technical Skills

• Strong Python and SQL. You write production code, not just notebooks
• Solid foundations in at least one ML area: ranking and recommendation systems, NLP and embeddings, classical ML, LLMs and agents, or causal inference
• Comfortable with modern LLM tooling: prompting, RAG, evaluation, tool use, structured outputs
• Practical stats: experiment design, dealing with confounding, knowing when an A/B test is broken
• Familiarity with our stack is a plus: FastAPI, PostgreSQL, BigQuery, FAISS, sentence-transformers, AWS, Amplitude
• Advanced degree in ML, CS, stats, or a related field is typical. PhD or research background is a strong bonus

Mindset

• Product first. You care about user impact more than novelty
• You use AI tools daily. Claude Code, Cursor, whatever ships faster
• You write things down. Memos, experiment results, design docs
• You're comfortable being the second dedicated ML person at the company and pushing the bar up
• You care about quality and follow through. You don't ship and forget

Why Join

• Ownership: You'll define the next chapter of ML at Sweatpals, not maintain someone else's models
• AI-native culture: We use Claude Code daily, ship fast, and treat AI tooling as table stakes
• Flexibility: Remote-first, async-friendly, EU timezone
• Compensation: Competitive salary plus early-stage equity

Our Values

• Celebrate Diversity of Thought: We embrace different backgrounds, opinions, and ways of thinking. We don't just welcome disagreement, we believe it makes the product better.
• Be a Leader: We take initiative, speak up, and drive things forward, no matter your title. Leadership is a mindset, not a level.
• Roll Up Our Sleeves: We do what it takes. No job is too small when we're building something big.
• Embrace Adventure: We stay curious, push boundaries, and see challenges as opportunities. Startups are a rollercoaster and we're here for the ride.
• Make Excellence the Baseline: We hold a high bar for quality and follow through. Doing great work is the starting point, not the finish line.

We're still early, which means your work will shape our path and our impact on communities and businesses everywhere. If you're excited by challenge, autonomy, and building something that matters, you'll feel at home here.

How to Apply

Send your resume (or LinkedIn) and a short note answering:

1. One ML system you shipped end-to-end and what it actually changed
2. A modeling decision you made that turned out to be wrong, and what you learned
3. A paper, blog post, or repo you keep going back to (optional, but we love this)