Machine Learning Engineer, AI Enablement

Translate scientific and business needs into actionable technical design and hands-on implementation of agentic AI solutions. Write, test, and ship high-quality, maintainable production code, and build scalable systems and data pipelines.

Contribute independently to defined projects by writing maintainable code and applying software engineering best practices (testing, CI/CD, documentation), alongside AI tooling and quantitative metrics to evaluate and scale ML systems.

Contribute to architectural decisions, code reviews, and the evolution of development processes.

Span the stack and contribute where needed, including outside your core area of expertise.

Stay up-to-date with emerging technologies and industry best practices, and adopt a culture of continuous learning, collaboration, and curiosity.

Roche’s Research and Early Development organizations at Genentech (gRED) and Pharma (pRED) have demonstrated how data and novel computational models can accelerate R&D and drive impact. The Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and AI to assist scientists in delivering innovative and transformative medicines for patients worldwide.

Within the CoE, the Data and Digital Catalyst (DDC) organisation drives the modernization of computational and data ecosystems and integration of digital technologies across Research & Early Development. The AI Enablement Engineering group builds scalable systems, data pipelines, and intelligent applications to enable scientists to work smarter, optimize model performance, and design end-to-end agentic workflows.

The team is cross-functional and impact-driven, values cognitive diversity and technical excellence, and partners with biologists, computational scientists, data experts, and researchers. The Machine Learning Engineer will also partner with computational scientists, ML experts, product leaders, and DevOps engineers.

• Master’s degree with 2+ years of professional experience or a PhD in Computer Science or a similar technical field.

• Value engineering best practices, such as testing, clean code, and documentation.

• Comfortable working across the ML lifecycle, from GPU optimization and model performance to backend systems and cloud-native architecture (e.g., Kubernetes, AWS).

• Solid knowledge of statistics and ML theory, as well as AI/ML frameworks, libraries, and toolsets.

• Collaborative engineering approach, including contributing to code reviews and architectural decisions and translating complex technical challenges into shared goals with diverse partners.

• Curiosity about new frameworks and languages; comfortable mentoring others and learning from them.

• Excellent communication skills, including translating technical complexity into clear, actionable insights for diverse audiences.

• Experience with biological data (ideally multimodal) and processes is a strong plus.

• Experience working with scientists or in a research environment is advantageous.

• Experience with workflow automation, GenAI, and/or agents is a plus.

• The role requires working onsite approximately 3 days a week and does not offer relocation assistance.