Applied Machine Learning Engineer, Visible

• You’ve trained ML models that made it into production and helped thousands of people
• You are excited to find ways to get 80% of the benefit from 20% of the effort, to push our understanding and our product forward at pace. You thrive in small mission-driven teams taking smart bets and learning as they go
• You invest time to understand the products you work on and their users, and are a valued voice in product discussions and ideation. You’re motivated to use your skills to help the more than 250,000 people using Visible to manage complex chronic illnesses
• You use Claude Code or Codex (or similar) regularly and know how to delegate effectively to AI agents
• You’re proactive and motivated to build a product you can be proud of: when you see how we could make something better, you make it happen
• You form views based on evidence and change them when the evidence changes, always in service of our members and our business

Desirable

• You’ll develop production-grade machine learning models in novel and emerging domains (e.g. predicting symptom flare-ups, treatment effectiveness, diagnosis assistance using extensive time-series datasets)
• You’ll perform exploratory data analysis on both product and health data, and occasionally assist with product experiments (mostly A/B tests, some quasi-experimental)
• You’ll report to Paul, our Data Science Lead, in a small, low-ego team of 11: three engineers, one QA (we're hiring a second), three designers, two data scientists, a behavioural scientist, and a PM. We're ambitious about what we're building and pragmatic about how we work
• You’ll support collaborations with researchers at top institutions (Imperial College London, Mount Sinai, Oxford, Yale) to inform robust, scientifically validated model development
• You’ll work with our flexible tech stack: GCP, BigQuery, Python, Postgres, and our time series database InfluxDB

• 1. Apply with your profile and optional cover letter
• 2. Introductory chat: A 30 minute introductory chat with Harry, our co-founder
• 3. Technical task and discussion: A 90-minute session where you’ll be given a task to work on for 30-40 minutes solo (we encourage the use of any AI tooling), then you’ll discuss your approach and results with our Data Science Lead. This is not a take-home task.
• 4. Product team interview: A 60-minute interview with two members of the wider product team to understand how you communicate across disciplines and solve problems collaboratively
• 5. Founder interview: A 30-minute interview with our founders Luke and Harry
• We're committed to running an accessible recruitment process. Let us know if there's anything we can do to support you through the process