Senior Machine Learning Engineer

Amgen is a biotechnology company focused on developing, manufacturing, and delivering medicines for serious illnesses. The Senior Machine Learning Engineer will build scalable AI proofs-of-concept, translate ambiguous business needs into practical AI solutions, and create reusable architectures and assets that support enterprise-scale production. The role also involves evaluating technical and business feasibility, partnering cross-functionally, and mentoring engineers.

Responsibilities:

• Build scalable AI proof-of-concepts that are designed to demonstrate a clear path from prototype to enterprise-scale solution
• Partner with product managers, business stakeholders, platform teams, and AI software engineers to translate ambiguous business needs into practical AI solutions with measurable value
• Design and implement modern AI systems using LLMs, agentic workflows, retrieval augmented generation, data pipelines, APIs, cloud-native services, and enterprise platforms
• Rapidly validate feasibility and value by assessing technical risk, data readiness, integration complexity, user experience, security considerations, performance, cost, and business impact
• Create reusable technical assets such as reference architectures, reusable components, documentation, decision records, and handoff materials that enable product, platform, or delivery teams to scale successful POCs
• Raise the technical bar for the team by modeling strong engineering practices, mentoring others, improving delivery patterns, and bringing clear ownership and accountability to uncertain, fast-moving work

Requirements:

• Doctorate degree
• Master's degree and 2 years of relevant experience
• Bachelor's degree and 4 years of relevant experience
• Associate's degree and 8 years of relevant experience
• High school diploma / GED and 10 years of relevant experience
• 4-6 years of relevant experience in AI engineering, machine learning engineering, software engineering, data engineering, cloud engineering, or related technical roles
• Demonstrated experience building full-stack AI-powered applications that move beyond experimentation and are designed with scalability, security, evaluation, and maintainability in mind
• Strong understanding of modern AI application architecture, including LLMs, retrieval-augmented generation, embeddings, vector databases, agentic workflows, tool use, orchestration frameworks, and AI evaluation methods
• Experience defining and applying evaluation methods for AI solutions, including accuracy, reliability, hallucination risk, latency, usability, safety, cost, and fitness for intended use
• Ability to translate ambiguous business problems into practical technical approaches, make sound tradeoffs, and rapidly validate feasibility, value, risks, and path to scale
• Experience with AWS Cloud, data pipelines, integration architecture, containers, CI/CD, observability, and secure development practices
• Strong software engineering foundation, preferably with Python and modern development practices, including testing, version control, modular design, documentation, and maintainable code
• Demonstrated ability to responsibly use AI tools to improve engineering productivity, explore technical solutions, automate repetitive tasks, and enhance the delivery of machine learning or software products
• Excellent communication, ownership, and cross-functional leadership skills, with the ability to partner effectively with product managers, business stakeholders, platform teams, and AI and software engineers