ML Engineer: Biosignals & Hormone Health Wearables

Clair Health, Inc.; Menlo Park (CA); On-site; CAD 184,000 - 240,000; Full time

Benefits offered by this job

Health, dental and vision insurance

401(k)

Paid time off

Catered team lunches

Equity

Clair device when it ships

Job summary

Clair Health, Inc. seeks a Machine Learning Engineer to turn raw biosignals into actionable insights for women. You’ll own parts of the system, validate models on real-world data, and ship improvements. You’ll work closely with founders to deliver products users see at launch.

You’ll build models for heart, sleep, activity and hormone insights, improve the AI agent, and contribute to production-grade code and study design. This is a hands-on, in-person role with a small team in Menlo Park.

Qualifications

• 2 to 5 years building ML systems that ran in production or on real users’ data.
• Strong Python, a modern deep learning framework, and the scientific Python stack.
• Experience with time-series or physiological data, such as PPG, ECG, IMU or EEG, or with LLM applications.
• Careful about evaluation. You split by subject, look for leakage, check calibration and test against simple baselines.

Responsibilities

• Biosignals and sensor models: own and improve models on raw signals and validate against real-world wearable data.
• Agents, voice and LLMs: improve Clair’s AI agent and voice experiences, extend evals and guardrails.
• Research to production: read papers, test methods, write production Python, and collaborate with MLOps and fullstack engineers.

Skills

Python

Deep learning

Time-series data

LLM applications

Production ML systems

Tools

MLOps

Cloud platforms

Data pipelines

Job description

Clair Health, Inc. seeks a Machine Learning Engineer to turn raw biosignals into actionable insights for women. You’ll own parts of the system, validate models on real-world data, and ship improvements. You’ll work closely with founders to deliver products users see at launch.

You’ll build models for heart, sleep, activity and hormone insights, improve the AI agent, and contribute to production-grade code and study design. This is a hands-on, in-person role with a small team in Menlo Park.