Senior Machine Learning Engineer, AI Studio

Career Category Engineering 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. Senior Machine Learning Engineer, AI Studio What you will do Let’s do this. Let’s change the world. In this vital role 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.

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, security zones and human review.  Lead hands-on development of production software, EDA, feature engineering, predictive models, deep-learning or NLP components, inference services, RAG, agent tools and workflow orchestration.  Establish baselines, experiment design, leakage controls, uncertainty, calibration, subgroup and robustness checks, gold sets, error taxonomies, expert adjudication and release thresholds.  Establish MLOps/LLMOps for lineage, reproducibility, versioning, CI/CD, canary or shadow release, observability, drift monitoring, SLOs, rollback, incidents, disaster recovery, capacity, cost and runbooks.  Coordinate security, privacy, Responsible AI, Quality, legal, model-risk and GxP controls; create reusable capabilities, measure adoption and value, mentor engineers and improve delivery practices. What we expect of you We are all different, yet we all use our unique contributions to serve patients. The Senior Machine Learning Engineer we seek is a seasoned professional with these qualifications.

Doctorate degree OR Master’s degree and 2 years of experience in Computer Science, IT or related fields OR   Bachelor’s degree and 4 years of experience in Computer Science, IT or related fields   OR   Associate’s degree and 8 years of experience in Computer Science, IT or related fields OR High school