Senior Machine Learning Engineer

About the role

Amgen's Artificial Intelligence & Data organization, the AI & Data Innovation Lab, is looking for a Senior Machine Learning Engineer to build high-impact AI proofs-of-concept that are credible, scalable, secure and ready for production. The role sits at the intersection of AI engineering, software engineering, cloud and data architecture, product thinking and business partnership. It suits a hands-on builder who partners with product managers and business stakeholders and raises the engineering bar for the team.

Key responsibilities

• Build scalable AI proofs-of-concept with a clear path from prototype to enterprise-scale solution.
• Partner with product managers, business stakeholders, platform teams and AI software engineers to turn ambiguous business needs into practical AI solutions.
• 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, UX, security, performance, cost and business impact.
• Create reusable assets such as reference architectures, components, documentation, decision records and handoff materials so product and delivery teams can scale successful POCs.
• Mentor others, model strong engineering practices and improve delivery patterns.

Required profile

• Basic qualification: doctorate, or master's degree with 2 years of relevant experience, or bachelor's degree with 4 years, or associate's degree with 8 years, or high school diploma with 10 years of relevant experience.
• Preferred: 4-6 years of experience in AI, machine learning, software, data or cloud engineering.
• Demonstrated experience building full-stack AI-powered applications with scalability, security, evaluation and maintainability in mind.
• Ability to translate ambiguous business problems into practical technical approaches and to make sound tradeoffs.

Required skills

• Strong understanding of modern AI architecture: LLMs, RAG, embeddings, vector databases, agentic workflows, tool use, orchestration frameworks and AI evaluation methods.
• Experience with AWS Cloud, data pipelines, containers, CI/CD, observability and secure development.
• Software engineering foundation, preferably Python, with testing, version control, modular design and documentation.
• Experience responsibly using AI tools to improve engineering productivity.

What we offer

• Annual salary range posted for the U.S. (excluding Puerto Rico), varying with skills, experience and qualifications.
• Health, dental and vision coverage, life and disability insurance, and a retirement savings plan with company contributions.
• Discretionary annual bonus or sales-based incentive plan, stock-based long-term incentives, and flexible work models where possible.