Senior Machine Learning Scientist

Department: Engineering

Location: Remote, Europe

Employment Type: FullTime

About Sweatpals

Sweatpals is the community-first fitness platform turning workouts into social experiences. Backed by a16z speedrun, Patron, Kevin Hart, Pear VC, and founders of Instacart and Dreamworks Animations, we connect hundreds of thousands of pals, hosts, and gyms through events, memberships, and social features. Were still scrappy at heart, but scaling fast.

We believe working out should be joyful, social, and inclusive, not just a solo grind. From run clubs and beach pilates to pickleball leagues and cold plunge socials, Sweatpals turns everyday workouts into meaningful social experiences.

Sweatpals also gives local leaders the tools to grow their fitness communities from side hustles to full-time, even million-dollar businesses. Hosts use our platform to run their business, from ticketing and memberships to marketing tools.

Were an AI-forward company. If youre excited about working at the intersection of ML, product, and a real marketplace, youll fit right in.

The Role

Were looking for a Senior Machine Learning Scientist to join the AI Squad and own the most ambitious ML work on our roadmap. Youll 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. Youll define problems, run the science, ship to production, and measure real user impact. You wont inherit a graveyard of half-finished notebooks. Youll build the next layer of ML at Sweatpals on top of what weve already shipped: semantic search, event tagging, collection ranking, retention models, and our LLM-powered HostCopilot and Front Desk Agent.

Youll spend your time on problems like:

• How do we rank events for each Pal so they discover more hosts theyd love?

• Can we predict churn early enough for HostCopilot to nudge before it happens?

• Whats 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?

What Youll Do

Modeling & Research

• 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 doesnt

• Own offline metrics (NDCG, recall@k, AUC, calibration) and tie them to online metrics (booking lift, retention, GMV)

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 Were 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

• Youre comfortable being the second dedicated ML person at the company and pushing the bar up

• You care about quality and follow through. You dont ship and forget

Why Join

• Ownership: Youll define the next chapter of ML at Sweatpals, not maintain someone elses 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 dont 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 were building something big.

• Embrace Adventure: We stay curious, push boundaries, and see challenges as opportunities. Startups are a rollercoaster and were 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.

Were still early, which means your work will shape our path and our impact on communities and businesses everywhere. If youre excited by challenge, autonomy, and building something that matters, youll 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)