Job Description

Machine Learning Engineer (Life Sciences)

12-Month Contract

Hybrid in Boston, MA

We are working with a well-established Life Sciences company whose mission is to transform the lives of people with serious diseases. Their team is seeking an MLE to build and scale the machine learning infrastructure behind their next-generation therapeutic discovery platforms. You'll work at the intersection of cutting-edge ML and translational science to accelerate the development of life-changing treatments for patients worldwide.

Responsibilities:

• Architect and implement ML systems that support therapeutic discovery platforms, with a focus on performance, scalability, and reliability in a clinical research environment.
• Analyze and optimize machine learning workflows by identifying bottlenecks and developing solutions to improve efficiency and throughput.
• Establish MLOps best practices across the organization, including experiment tracking, model versioning, and automated deployment pipelines.
• Lead the integration of ML systems with broader software and data infrastructure to enable seamless data flow and interoperability across the discovery pipeline.

Requirements:

• Demonstrated experience designing, building, and optimizing end-to-end machine learning systems, particularly for biological or clinical research data.
• Deep understanding of machine learning techniques, including experience applying deep learning to complex, high-dimensional biological datasets.
• Strong domain knowledge in areas such as cell biology, genetics, immunology, or related fields within life sciences or biomedical research.
• Advanced proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Proven track record of implementing robust data infrastructure and tooling to support scalable ML workflows in a scientific or healthcare setting.

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