Machine Learning Engineer, Infra, AI for Drug Discovery

Genentech is a company focused on innovation in healthcare, specifically in drug discovery. The Machine Learning Engineer will design and operate scalable model-serving infrastructure for machine learning workloads, collaborating with cross-functional teams to enhance platform reliability and usability.

Responsibilities:

• Design, implement, ship, and operate scalable model-serving infrastructure for machine learning, scientific, LLM, and agentic workloads
• Help evolve our internal model deployment platform into a reliable, self-service platform for teams across the organization
• Improve platform scalability and reliability, including scale-to-zero, faster model startup, workload isolation, traffic management, and reduction of request failures and latency bottlenecks
• Build observability and operational tooling for model usage, latency, reliability, resource consumption, inference cost, bottlenecks, and service-level indicators
• Improve the usability of model deployment by developing validated configuration interfaces, reusable deployment patterns, APIs, command-line tools, and documentation
• Help converge real-time and batch inference workflows onto shared platform capabilities where appropriate
• Contribute to model lifecycle management infrastructure, including model registration and versioning, evaluation, promotion and release gates, monitoring, environment progression, and rollback
• Build event-driven integrations that connect model publication, evaluation, promotion, deployment, and retraining workflows
• Build consistent metrics and evaluation signals for understanding model cost, quality, reliability, and fitness for downstream workflows
• Partner with machine learning, data, scientific, and platform teams to translate requirements into maintainable solutions and remove infrastructure bottlenecks
• Own workstreams from design through implementation and production support, using strong software-engineering practices including testing, reviews, documentation, and incremental delivery