Develops and deploys machine learning models for biomedical research and product applications, collaborating with scientific, engineering, and product teams. The role spans independent delivery through technical leadership and requires Python, ML frameworks, deployment pipelines, and relevant graduate-level experience.
107k – 200k/yr
On-site5+ YOEML Engineering
About the role
Responsibilities
Design, develop, and deploy machine learning models for research and product development projects.
Collaborate with scientists, engineers, and product teams to translate biological and clinical requirements into scalable machine learning solutions.
Contribute to experimental design and analysis, including ideation, documentation, and reporting.
Participate in design reviews, journal clubs, machine learning best-practice initiatives, and governance activities.
Improve machine learning pipelines and infrastructure with MLOps and platform teams.
Publish and present scientific work, including abstracts, manuscripts, and conference contributions.
Level-Specific Expectations
Machine Learning Engineer II
Independently deliver projects.
Improve processes and mentor junior engineers.
Machine Learning Engineer III
Lead initiatives end to end.
Set technical direction.
Identify opportunities with clear business and scientific impact.
Requirements
Machine Learning Engineer II
Master’s degree plus 2–4 years of experience, or Ph.D. with 0–2 years of experience.
Proven track record developing and deploying machine learning models into production or research applications.
Strong proficiency in Python, machine learning frameworks, and data pipeline development.
Ability to work independently, contribute to experimental design, and improve machine learning workflows.
Strong communication and cross-functional collaboration skills.
Machine Learning Engineer III
Master’s degree plus 5+ years of experience, or Ph.D. with 3+ years of experience.
Deep expertise in machine learning, computer vision, or biomedical AI, with high-impact contributions through publications, open source, or products.
Mastery of machine learning frameworks, software engineering best practices, and deployment pipelines.
Ability to lead end-to-end projects, mentor others, and set technical direction.
Experience connecting technical improvements to business or clinical impact.
Strong contributions to scientific strategy, including abstracts, manuscripts, and conference presentations.
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