Machine Learning Engineer (Foundation Models & Personalization)

About the role

This is a Machine Learning Engineer (Foundation Models and Personalisation) opportunity with a Series D consumer sleep and recovery technology company, combining hardware, software and AI. It is not healthtech in the provider sense. You’ll build and deploy ML models for personalisation, prediction and behaviour understanding (readiness forecasting, event detection, individual recommendations), apply LLMs and multimodal modelling to product workflows, model user behaviour from longitudinal sleep and environment data, design offline evaluation and online experiments, and productionise training and inference pipelines with monitoring and drift detection. The stack includes PyTorch, TensorFlow or JAX, Python, SQL, and Spark or Ray. The ideal candidate will have 2+ years building ML systems in production, ideally for consumer products, with strong fundamentals in supervised learning, time-series modelling and deep learning, and experience with personalisation systems. Health or biometric data and privacy-preserving methods would be advantageous. Location is not stated in the posting; the sheet shows the San Francisco Bay Area. We're looking for someone curious, pragmatic, and eager to work across the stack with a small senior team. You'll own end-to-end delivery of major features, influence architecture, and mentor engineers.

What you'll do

• Ship customer-facing features on a weekly cadence.
• Partner with design and product on scoping and trade-offs.
• Drive technical quality through review, testing, and docs.
• Mentor one or two engineers on your team.

Requirements