Machine Learning Engineer, AI Studio
About The Position
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. Machine Learning Engineer, AI Studio What you will do Let’s do this. Let’s change the world. In this vital role you will independently own defined production components within enterprise AI products and automation solutions. You will design, release, diagnose and support the component while connecting technical measures to user and workflow outcomes. Within Applied AI, AI Studio turns 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.
Requirements
- Master’s degree OR Bachelor’s degree and 2 years of Computer Science, IT or related field experience OR Associate’s degree and 6 years of Computer Science, IT or related field experience OR High school diploma / GED and 8 years of Computer Science, IT or related field experience.
- Demonstrated ownership of at least one production software, data, ML, GenAI or automation component.
- Strong hands-on proficiency in Python and SQL, with sound software-engineering and testing practices.
- Strong capability in at least one of classical ML, GenAI/RAG/agents or MLOps/platform engineering, with working knowledge of adjacent areas.
- Independent problem solving and sound component-level technical judgment.
- Clear communication of assumptions, evidence, trade-offs, risks and support implications.
- Strong collaboration with business SMEs, product, architecture, software, data, platform, evaluation and control partners.
- Ownership, reliability and disciplined follow-through from design through production support.
- Ability to guide junior engineers and learn new tools through evidence-based experimentation.
Nice To Haves
- Advanced ML and deep learning: Experience with PyTorch, TensorFlow, Hugging Face, scikit-learn, XGBoost, PyMC, computer vision, NLP, GNNs, causal inference or uncertainty estimation.
- Advanced GenAI and knowledge systems: Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, hybrid retrieval, knowledge graphs, graph RAG or evidence verification.
- Cloud, data and MLOps: Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, infrastructure as code, MLflow, Airflow, Kubeflow or GitHub Actions.
- Agents and human-AI workflows: Familiarity with MCP-style integration, agent tracing, adversarial testing, durable workflows, permissions, human review, BI or process automation.
- Regulated enterprise delivery: Experience in healthcare, life sciences, GxP, validated systems or another regulated or high-impact environment.
Responsibilities
- Define component boundaries, intended use, acceptance criteria, non-functional requirements, decision consequences, support expectations and technical estimates with product and architecture partners.
- Design and implement maintainable Python, SQL, API, data, model, retrieval, agent-tool and workflow components with clear contracts, configuration, testing, error handling and documentation.
- Apply EDA, feature engineering, supervised or unsupervised methods, baselines, cross-validation, leakage prevention, calibration, subgroup, threshold, explainability and error analysis where relevant.
- Build GenAI, NLP, RAG and bounded agent components using structured output, embeddings, hybrid search, reranking, provenance, citations, permissions, approvals, retries and recoverable failure behaviour.
- Engineer batch or event-driven data, document, feature, embedding, label and evaluation pipelines with schema validation, lineage, provenance, access control and consistency checks.
- Define representative evaluation for model quality, uncertainty, retrieval, grounding, citations, task success, tool correctness, safety, latency, cost and user impact.
- Release and support components using cloud services, containers, CI/CD, versioning, monitoring, rollback, incident response and runbooks; lead diagnosis of moderately complex failures.
- Apply security, privacy, Responsible AI, validation, auditability, human oversight and applicable GxP controls; contribute reusable assets and guide Associate engineers on familiar work.
Benefits
- A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
- A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
- Stock-based long-term incentives
- Award-winning time-off plans
- Flexible work models where possible.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Mid Level
