Top Healthtech Machine Learning Engineer Jobs

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

Lead and mentor ML engineers and data scientists, overseeing ML strategy and development of scalable models for healthcare applications.

The role involves developing machine learning models for robotics in medical imaging, collaborating with engineers and clinicians to prototype and deploy systems for autonomous surgical interventions.

Build and evaluate AI agents and ML models for medical billing, collaborating with operations and customers to improve outcomes.

Seeking an AI/ML Engineer to own the LLM lifecycle, optimize RAG systems, and collaborate on AI strategy in a hybrid team.

Design and build agentic LLM pipelines and full-stack AI features. Implement Python/FastAPI backends and TypeScript/React tooling, prompt-engineer models, develop eval datasets and observability, and partner with product teams to deploy reliable production LLM workflows.

As a Senior Machine Learning Engineer, you will develop and deploy AI-driven products to enhance clinical trials, collaborating with teams to improve patient care through scalable machine learning solutions.

Lead ML work for send-time, channel, and propensity optimization to increase engagement. Design and ship recommendation/sequential-decision systems, ensure experimental rigor, own production models end-to-end, and mentor a small ML team while partnering with product, engineering, and growth.

Conduct applied research using signal processing and machine learning to develop predictive ECG and cardiac monitoring algorithms. Collaborate with clinicians and engineers to define requirements, design training/testing/deployment architectures, manage R&D projects, mentor others, and promote development best practices to produce fieldable medical device solutions.

Design, train, and deploy large-scale AI/ML models for multimodal biological data; build research-grade ML infrastructure and data pipelines; collaborate with scientists and engineers; establish best practices and community standards; publish and open-source methods and code.