Top Biotech Machine Learning Engineer Jobs

Design, implement, validate, and deploy advanced machine learning and statistical models across the data science pipeline. Collaborate with product and engineering, mentor peers, translate research into production, and deliver high-impact business insights using large-scale and distributed data platforms.

Responsible for bioinformatics ML/AI application development in clinical settings, designing and deploying ML/AI solutions, and collaborating with multidisciplinary teams.

The R&D Engineer Intern will design machine learning pipelines, translate research into products, and develop microservices for biomedical engineering applications.

Contribute to developing machine learning models for multi-scale biology, focusing on design, deployment and optimization across teams and technologies.

The role involves applying machine learning in drug discovery, leading model development, collaborating with cross-functional teams, and mentoring staff.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

The Machine Learning Researcher will develop new architectures for data-efficient model induction, focusing on optimizing generalization performance and runtime inference in networks, while contributing to a performance-driven code base.

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.

High-ownership full‑stack role building a clinician Next.js/React portal, Python/Flask order service (Postgres), and a slide-tiles ML/CV service producing DeepZoom pyramids. Work spans product code, ML/CV inference, GCP/Modal deployments, integrations, and rigorous testing/CI practices.

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.