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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Scientist - **Company:** Sweatpals - **Location:** Eu, France (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, BigQuery, Python (Programming Language), PostgreSQL, Machine Learning, Recommender Systems, Search Technologies, SQL Databases, Large Language Models, Fastapi, Production Code, Amplitude Analytics - **Published:** May 31, 2026 - **Apply:** https://fr.indeed.com/viewjob?jk=d59d7003db8d97ff ## About the Role Do you have experience in SQL?, Do you have a Doctoral degree?, * 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 ## Description We're looking for a Senior Machine Learning Scientist to join the AI Squad and own the most ambitious ML work on our roadmap. You'll report to the Head of AI and partner closely with engineering and product to ship models that move our marketplace. This is a high-ownership role. You'll define problems, run the science, ship to production, and measure real user impact. You won't inherit a graveyard of half-finished notebooks. You'll build the next layer of ML at Sweatpals on top of what we've already shipped: semantic search, event tagging, collection ranking, retention models, and our LLM-powered HostCopilot and Front Desk Agent. You'll spend your time on problems like: * How do we rank events for each Pal so they discover more hosts they'd love? * Can we predict churn early enough for HostCopilot to nudge before it happens? * What's the right way to price a class or membership to maximize host GMV without hurting bookings? * How do we make LLMs reliable enough to draft host campaigns, recommend events, and answer questions at a real front desk? * How do we measure if our models actually move the marketplace, not just CTR?, * Frame fuzzy product problems as ML problems and pick the right approach: ranking, retrieval, classification, sequence models, LLM agents, or classic stats * Run end-to-end: data exploration, offline evaluation, prototype, online experiment, iteration * Push to the cutting edge when it matters, stay pragmatic when it doesn't * Own offline metrics (NDCG, recall@k, AUC, calibration) and tie them to online metrics (booking lift, retention, GMV), * 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, * 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 ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [Optimizing Discovery: PostgreSQL's Role in Transforming GetYourGuide's Search](https://www.wearedevelopers.com/videos/1647-optimizing-discovery-postgresql-s-role-in-transforming-getyourguide-s-search) - [Making Data Warehouses fast. 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