Principal Machine Learning Engineer

Career Category Information Systems Job Description Join Amgen’s Mission of Serving Patients At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do. Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. Amgen is advancing a broad and deep pipeline of medicines to treat cancer, heart disease, inflammatory conditions, rare diseases, and obesity and obesity-related conditions. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives. Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career. Principal Machine Learning Engineer What you will do Let’s do this. Let’s change the world. In this vital role you will Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem. We are seeking a Principal Machine Learning Engineer —Amgen’s most senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI solutions. Sitting at the intersection of engineering excellence and data-science enablement, you will develop, deploy and monitor models—classical ML, deep learning and LLMs—securely and cost-effectively. Acting as a “player-coach,” you will establish AI solution strategy, define technical standards, and partner with DevOps, Security, Compliance and Product teams to deliver a frictionless, enterprise-grade AI solutions. Roles & Responsibilities: Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem. Build production ML/GenAI solutions and lightweight apps delivering sub-second insights. Build end-to-end ML pipelines —data ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotion—using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks. 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. Establish observability, SLOs, and safe deploys (blue-green/canary, shadow, rollbacks) with incident runbooks. Lead rigorous evaluation (offline/online, A/B), drift detection, and automated retraining. Architect LLM/RAG with prompt management, safety guardrails, and optimized inference. Enforce data quality , lineage, and model/data cards; apply privacy-preserving techniques where needed. Contribute reusable ML/GenAI components —feature stores, model registries, experiment-tracking libraries—and 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. Prototype and benchmark new algorithms , offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs. Translate domain needs (R&D, Manufacturing, Commercial) into roadmaps; mentor teams and communicate trade-offs. What we expect of you We are all different, yet we all use our unique contributions to serve patients. The professional we seek is a Principal Machine Learning Engineer with these qualifications. Basic Qualifications: Doctorate degree and 2 years of Machine Learning Engineer experience OR Master’s degree and 6 years of Machine Learning Engineer experience OR Bachelor’s degree and 8 years of Machine Learning Engineer experience OR Associate’s degree and 10 years of Machine Learning Engineer experience OR High school diploma / GED and 12 years of Machine Learning Engineer experience In addition to meeting at least one of the above requirements, you must have a minimum of 2 years experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation or resources. Your managerial experience may run concurrently with the required technical experience referenced above 3-5 years in AI/ML and enterprise software. Strong command of machine-learning algorithms — regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques—with the judgment to choose, tune and operationalize the right method for a given business problem. Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale. Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, LangGraph, Semantic Kernel). Proficiency in Python and Java; containerization (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines). Strong business-case skills—able to model TCO vs. NPV and present trade-offs to executives. Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives. Preferred Qualifications: Experience in Biotechnology or pharma industry is a big plus Published thought-leadership or conference talks on enterprise GenAI adoption. Master’s degree in Computer Science and or Data Science <