About this role
Sr Machine Learning Engineer
CAREER LEVEL: GCF 5 – Specialist
CAREER TRACK: Individual Contributor
PRIMARY SCOPE: End-to-end ownership of a small AI asset or substantial technical workstream
ORGANIZATION: Applied AI | AI Studio
ABOUT AMGEN
Amgen harnesses the best of biology and technology to fight the world’s toughest diseases and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains at the cutting edge of innovation, using technology and human genetic data to push beyond what is known today.
ABOUT THE ROLE
Role Description:
The Senior Machine Learning Engineer position offers a unique opportunity to join a fun, innovative engineering team within the AI & Data Science (AI&D) - organization. We are the Applied AI team (AI Studio). AI Studio is Amgen’s enterprise engine for turning high-value business challenges into scalable AI products. We partner with key business partners across the company to identify the right opportunities, shape them into actionable use cases, and design, build, and launch AI products responsibly. Our work spans the full lifecycle—from early discovery and rapid prototyping to production deployment, reuse across the enterprise, and measurable business impact. You will be part of AI Studio and define and own AI assets or substantial technical workstream from problem framing through architecture, model and system development, evaluation, launch, stabilization, support transition, adoption and measurable outcome.
You will remain hands-on while leading decisions across software, statistics, classical ML, deep learning, NLP, GenAI, RAG, bounded agents, data and knowledge pipelines, APIs, MLOps/LLMOps, security, governance and operations. Within Applied AI - AI Studio turn prioritized business demand into governed, reusable AI assets with accountable ownership and measurable value across software, data, automation, machine learning, Generative AI, RAG, bounded agents, evaluation, observability and lifecycle operations.
Roles & Responsibilities:
Define the user, workflow, decision, intended use, baseline, value hypothesis, acceptance criteria, adoption path, operating owner and measurable technical and business outcomes.
Map rules, exceptions, data dependencies and human decision points before selecting deterministic automation, classical ML, deep learning, GenAI, RAG, agents or a manual approach.
Own production architecture across data, feature and knowledge pipelines, models, retrieval, agents, APIs, persistence, workflows, user experience,