Top Healthtech Machine Learning Engineer Jobs

As a Senior Machine Learning Software Engineer, you will design, deploy, and optimize ML solutions in healthcare, working with AI scientists and ensuring compliance with regulations.

Design, train, and evaluate machine learning models to match patients with therapists, improving clinical outcomes and enabling tailored care.

Own AI product strategy and roadmap across clinician- and member-facing surfaces. Partner with engineering, applied ML, design, and clinical teams to ship, scale, and measure LLM/ML-powered features; set safety and quality guardrails, run evals, make model build-vs-buy decisions, and translate ML capability into clinical workflow outcomes.

Lead architecture, development, and scaling of an AI-native answering service. Hands-on director who authors and reviews code, integrates LLMs and telephony, ensures reliability and low latency, hires and mentors engineers, manages vendors, defines KPIs, and aligns technical roadmap with product and compliance requirements.

The AI/Machine Learning Engineer will design, develop, and deploy AI models for healthcare, collaborating with teams to enhance patient outcomes using advanced data analytics and machine learning techniques.

The Senior Machine Learning Research Engineer will develop and deploy deep learning pipelines, optimize model training, and collaborate with scientists to enhance AI/ML capabilities for cancer detection.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

Embed with product/tech cohorts to build and ship Claude Code plugins, skills, hooks, and MCP integrations; generalize work into reusable platform artifacts; integrate services via the MCP Gateway and AWS; support adoption, documentation, and on-call for marketplace artifacts while partnering on security, auth, and observability.

Build and deploy AI agents and automations to streamline commercial and operational workflows. Work with stakeholders to identify opportunities, prototype LLM-based solutions, integrate via REST APIs, and maintain documentation and monitoring. Serve as technical liaison to business teams and iterate quickly on deployed tools.

The Machine Learning Engineer will design scalable systems, deploy production ML models, and develop distributed data pipelines, collaborating across teams.