ML Infra Engineer: Scalable Model Serving & MLOps

F. Hoffmann-La Roche AG; South San Francisco (CA); On-site; USD 147,600 - 274,000; Full time

Benefits offered by this job

Discretionary annual bonus

Job summary

Genentech is seeking a Machine Learning Infrastructure Engineer to design, implement, and operate scalable model-serving infrastructure for ML, scientific, LLM, and agentic workloads. You will evolve a self-service platform, improve scalability, and build observability tools for cost and reliability metrics.

You will collaborate with ML, data, and platform teams to deliver maintainable solutions, own end‑to‑end design-to-delivery workstreams, and contribute to production-grade deployment

Qualifications

• BS or MS in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

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.

Skills

Python

Cloud platforms (AWS)

Kubernetes

CI/CD

Observability tooling

Distributed systems

Education

BS in Computer Science or related field

Tools

Kubernetes

Terraform

Pulumi

Datadog

Prometheus

Grafana

OpenTelemetry

Dagster

Job description

Genentech is seeking a Machine Learning Infrastructure Engineer to design, implement, and operate scalable model-serving infrastructure for ML, scientific, LLM, and agentic workloads. You will evolve a self-service platform, improve scalability, and build observability tools for cost and reliability metrics.

You will collaborate with ML, data, and platform teams to deliver maintainable solutions, own end‑to‑end design-to-delivery workstreams, and contribute to production-grade deployment