Top Biotech Machine Learning Engineer Jobs

The Scientist, AI Engineer role involves designing and deploying machine learning models for drug discovery, collaborating with cross-functional teams, and leveraging proprietary data to build AI-powered tools for medicinal chemistry and biology.

Develop and maintain a full-stack platform for computational chemists, focusing on drug discovery through machine learning and data infrastructure. Collaborate with interdisciplinary teams to enhance software tools for visualizing molecules and managing workflows.

The Senior ML Engineer will develop AI solutions for healthcare data, collaborating with various teams, contributing to AI strategy, mentoring, and maintaining expertise in AI technologies.

The role involves designing ML systems using multi-omics data, developing causal models, collaborating with scientists, and optimizing ML methods.

As a Senior Machine Learning Scientist, you will develop multimodal foundation models using large-scale single-cell datasets to advance drug discovery, applying deep learning methods and collaborating with biologists and engineers.

Leading the development of ML models for drug discovery, collaborating with multidisciplinary teams, and managing team members while ensuring data-driven decision-making.

The Machine Learning Scientist will use insitro's TherML platform to develop oligonucleotide designs and collaborate across teams for drug discovery.

The role involves developing, deploying, and mentoring in machine learning models for bioprocess development, while collaborating with scientists and engineers.

The candidate will develop algorithms for optimizing therapeutic mRNAs, collaborate with teams on data acquisition, and communicate findings. A Ph.D. in a relevant field is required along with experience in machine learning and bioinformatics.

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.