Machine Learning Engineer, AI Studio

• 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.

What we expect of you

We are all different, yet we all use our unique contributions to serve patients. The Machine Learning Engineer we seek is a professional with these qualifications.

• Production software and AI/ML system design: Python and SQL modules, APIs, background jobs, event flows, testing, performance, observability, source control and maintainable failure semantics.
• Statistics, modelling and experimentation: EDA, feature engineering, classification, regression, clustering, ensembles, cross-validation, leakage prevention, calibration, uncertainty and decision-aware error analysis.
• GenAI, RAG, knowledge and agents: Prompt and context management, structured output, chunking, embeddings, hybrid retrieval, reranking, citations, access-aware retrieval, tool schemas and human approval.
• Data, knowledge and cloud-scale systems: Batch and event-driven pipelines, contracts, lineage, provenance, relational/document/graph/vector stores, APIs, containers, Spark or Databricks and cloud-native services.
• 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.
• 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.
• 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.

As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.

The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications. In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include:

• 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. Refer to the Work Location Type in the job posting to see if this applies.