Machine Learning Engineer (AI Enablement)
About The Position
The Machine Learning Engineer will join Roche’s Engineering - AI Enablement group within the Data and Digital Catalyst organization, part of the Computational Sciences Center of Excellence. The team enables AI across Research and Early Development by supporting AI adoption, building and deploying AI-based solutions, scaling model training and inference, evaluating AI/ML models, and developing applications and utilities for scientific work. Its goal is to make AI/ML a practical everyday resource for activities such as data analysis, literature search, and documentation. The role works closely with computational and research scientists, AI/ML experts, product leaders, DevOps, and other partners. The engineer will build, own, and continually improve scalable AI/ML systems that use scientific data to accelerate drug discovery and development. Onsite presence at the South San Francisco campus is expected at least three days per week. Relocation assistance is not available. The position is open at SE5 and SE6 levels, with final leveling determined after interviews. The stated salary range for the primary San Francisco, California location is $147,600–$274,000. A discretionary annual bonus may be available based on individual and company performance.
Requirements
- Bachelor’s degree and 1–3 years of experience with AI/ML frameworks, libraries, and toolsets.
- Expert knowledge of statistics, machine learning theory, and algorithms.
- Strong knowledge of machine learning performance optimization and GPU best practices.
- Experience with Kubernetes and relational databases, NoSQL databases, or data lakes.
- Experience with cloud-native architectures in public clouds; AWS is preferred.
- Demonstrated understanding and application of engineering best practices.
- Excellent communication skills and ability to build trusted partnerships with internal and external collaborators.
- Ability to quickly learn new technologies and programming languages, and a passion for continuous learning.
Nice To Haves
- Experience with biological data, ideally multimodal, and scientific processes.
- Experience working with scientists or in a research environment.
- Experience with workflow automation, generative AI, and/or agents.
- Experience with AWS, noted as an ideal public-cloud platform.
Responsibilities
- Design, develop, and test robust, scalable, maintainable AI/ML-facing scientific web applications and backend systems.
- Build tools to evaluate AI/ML model performance and develop new ways to understand AI quality.
- Partner with product managers and scientists to understand user needs, shape requirements, and translate them into technical specifications.
- Develop and maintain systems to collect, structure, and store scientific data for advanced analytics, machine learning, and other data-driven initiatives.
- Implement, adopt, or evaluate AI/ML algorithms and analytical techniques.
- Contribute to architectural decisions, code reviews, and improvements to development processes.
- Work across the technology stack and contribute outside the core area of expertise when needed.
- Keep up with emerging technologies and industry practices, applying continuous learning, collaboration, and curiosity.
Benefits
- A discretionary annual bonus may be available based on individual and company performance.
- Benefits are referenced in the posting, but specific benefits are not listed.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
