Top Biotech Machine Learning Engineer Jobs

Lead design, development, and deployment of enterprise AI agents and multi-agent systems. Architect scalable, secure AI/data systems, integrate vector DBs, embeddings, RAG, and cloud MLOps, drive automation/APIs, mentor engineers, set KPIs, and ensure observability, ethical AI, and production readiness.

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.

As a Machine Learning Engineer Intern, you'll develop neural decoders for Brain-Computer Interfaces and collaborate with cross-functional teams on new functionalities.

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.

Lead and grow an algorithm science team to develop and productize sensor-based health sensing algorithms. Set strategy, manage cross-functional delivery with hardware/firmware/product partners, maintain technical depth, and build scalable evaluation pipelines and processes.

Develop machine learning methods for 3D particle detection and structural analysis in cryo-electron tomography data, collaborating with interdisciplinary teams on scientific applications.

Lead the development of machine learning and bioinformatics for NGS products, collaborating cross-functionally to analyze complex datasets, and mentor junior members.

Own stability, performance, and reliability of AI-backed backend systems. Translate customer problems into simple solutions, ship production-quality Django/Python services, collaborate with Product and Design, and manage complexity while delivering product value.

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

Lead the scientific direction and machine learning development for climate modeling. Manage model training, evaluation, and collaboration across teams to develop predictive systems based on hydrology and climate data.