1 day ago
Base Salary
$193k - $358k/yr
Responsibilities
- Own the MLOps/AgentOps stack for foundation models, including evaluation harnesses, experiment tracking, observability, monitoring, CI/CD, and infrastructure.
- Architect and deploy autonomous agents that use tools, retrieve scientific evidence, and execute multi-step reasoning across drug discovery workflows.
- Design agent memory architectures, context management, and interfaces to genomic, chemical, and clinical data sources.
- Build and optimize distributed training and inference systems for foundation models and productionize Python/PyTorch codebases.
- Define the engineering roadmap for agentic and foundation models and provide technical leadership on ML infrastructure.
- Translate scientific problems and reasoning objectives into efficient, shippable systems in partnership with ML scientists and domain experts.
Requirements
- BS, MS, or PhD in Computer Science, Machine Learning, Engineering, or a related quantitative field, with 5+ years for a PhD, 8+ years for an MS, or 10+ years for a BS.
- Demonstrated technical leadership and experience building, shipping, and owning large-scale ML systems and infrastructure end-to-end.
- Exceptional Python skills and strong software engineering fundamentals, including Git, automated testing, documentation, and architecture design.
- Hands-on experience with modern deep-learning frameworks such as PyTorch and JAX and deploying ML infrastructure on AWS or HPC environments, including distributed training tools.
- Practical experience designing agent orchestration frameworks, including LangGraph or MCP-based tool integration, persistent agent memory, and self-improving loops.
- Strong interest in frontier AI, agentic science, AI for drug discovery, biology, and chemistry.
- Preferred: expertise in LLM serving, test-time compute, sampling and search strategies, model routing, batching, caching, and latency/cost/quality tradeoffs.
- Preferred: experience with protein sequences, chemical graphs, or structured molecular data.
- Preferred: public contributions to open-source ML, systems, or MLOps libraries.
Benefits
- Onsite presence at the Roche/Genentech campus is expected.
- Relocation benefits are not available for this posting.
- A discretionary annual bonus may be available based on individual and company performance.
- The position qualifies for the company benefits described at the provided benefits link.
Categories
About Genentech
Genentech discovers, develops, and commercializes biologic therapies for serious diseases across oncology, immunology, neuroscience, ophthalmology, infectious disease, and metabolism. Founded in 1976 and headquartered in South San Francisco, it operates as a member of the Roche Group, with research centered in its gRED organization and revenue driven by prescription drug sales and collaborations.