Sr Machine Learning Engineer -AI / ML- US Remote (,, United States)

Sr Machine Learning Engineer

We are seeking a Sr Machine Learning EngineerAmgen's senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI platforms. Sitting at the intersection of engineering excellence and data-science enablement, you will design the core services, infrastructure, and governance controls that allow hundreds of practitioners to prototype, deploy, and monitor modelsclassical ML, deep learning, and LLMssecurely and cost-effectively. Acting as a player-coach, you will establish platform strategy, define technical standards, and partner with DevOps, Security, Compliance, and Product teams to deliver a frictionless, enterprise-grade AI developer experience.

Roles & Responsibilities :

• Engineer end-to-end ML pipelinesdata ingestion, feature engineering, training, hyper-parameter optimization, evaluation, registration, and automated promotionusing 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.
• Optimize performance and cost at scaleselecting appropriate algorithms (gradient-boosted trees, transformers, time-series models, classical statistics), applying quantization / pruning, and tuning GPU / CPU auto-scaling policies to meet strict SLA targets.
• Instrument comprehensive observabilityreal-time metrics, distributed tracing, drift & bias detection, and user-behavior analyticsenabling 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 componentsfeature stores, model registries, experiment-tracking librariesand evangelize 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.

Must-Have Skills :

Good-to-Have Skills :

Education and Professional Certifications :

Soft Skills :