Amgen is seeking a Sr Machine Learning Engineer to build and scale end-to-end machine-learning and generative-AI platforms. The ideal candidate will have 3-5 years of experience in AI/ML and enterprise software, and a comprehensive command of machine-learning algorithms. They will be responsible for designing core services, infrastructure, and governance controls, as well as partnering with DevOps, Security, Compliance, and Product teams to deliver a frictionless, enterprise-grade AI developer experience.
Requirements
- Engineer end-to-end ML pipelines using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks.
- Harden research code into production-grade micro-services, packaging models in Docker/Kubernetes and exposing secure REST, gRPC or event-driven APIs for consumption by downstream applications.
- Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency.
- Optimise performance and cost at scale—selecting appropriate algorithms (gradient-boosted trees, transformers, time-series models, classical statistics), applying quantisation/pruning, and tuning GPU/CPU auto-scaling policies to meet strict SLA targets.
- Instrument comprehensive observability—real-time metrics, distributed tracing, drift & bias detection and user-behaviour analytics—enabling rapid diagnosis and continuous improvement of live models and applications.
- Embed security and responsible-AI controls (data encryption, access policies, lineage tracking, explainability and bias monitoring) in partnership with Security, Privacy and Compliance teams.
- Contribute reusable platform components—feature stores, model registries, experiment-tracking libraries—and evangelise best practices that raise engineering velocity across squads.
- Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness.
- Partner with data scientists to prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs.
Benefits
- 401k Matching
- Retirement Plan
- Visa Sponsorship
- Generous Paid Time Off
- Generous Parental Leave
- Tuition Reimbursement
- Relocation Assistance
