Top Biotech Machine Learning Engineer Jobs

Develop, validate, and interpret ML models and end-to-end pipelines to predict oral drug absorption using large, heterogeneous biopharmaceutics and clinical datasets. Emphasize model explainability, uncertainty quantification, and mechanistic/physics-informed approaches, translate outputs into actionable insights for drug development teams, and communicate results via reports, presentations, and publications while contributing to AI-enabled predictive platforms.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

The Machine Learning Engineer designs and optimizes algorithms for data analysis and workflow automation, collaborating with engineering teams and ensuring algorithm performance on edge devices and high-performance environments.

As a Senior AI/ML Engineer, you will design and deploy LLM systems, collaborate with cross-functional teams, and manage model deployment in production environments.

The Senior Engineer II, Machine Learning will develop and implement machine learning models, analyze multiomic/single-cell data, and apply image processing techniques for research projects.

The Postdoctoral Researcher at Zhou Lab will develop machine learning models for biological data, specifically in genomics and gene regulation, collaborate with experimental biologists, and lead research projects for significant scientific discoveries.

Lead development of predictive maintenance and reliability analytics across manufacturing sites. Build predictive models, dashboards, and asset health tools using maintenance, historian, and operational data to reduce downtime, optimize maintenance strategies, and quantify operational and financial benefits. Partner with reliability, maintenance, engineering, and technology teams to implement digital reliability solutions and support reliability governance and continuous improvement.

As a Machine Learning Engineer/Scientist, you will develop ML systems for cryopreservation research, manage data pipelines, and deploy models for scientific insight.

Design and deliver end-to-end AI and optimization solutions for industrial and scientific data. Apply LLMs, multimodal models, knowledge graphs, and RAG to build production-ready tools, pipelines, and user-facing analytics. Collaborate with domain experts, validate and monitor models, and ensure maintainability, governance, and measurable business impact in operational environments.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.