Machine Learning Engineer, AI Studio
Amgen
August 25, 2026
Remote friendly (United States)
United States
IT
Machine Learning Engineer, AI Studio
What You Will Do
- Independently own defined production components within enterprise AI products and automation solutions.
- Design, build, release, diagnose, and support components that connect technical measures to user and workflow outcomes.
- Contribute to governed, reusable AI assets across the AI lifecycle (discovery/prototyping to production, reuse, and measurable business impact).
- Components may include: model/inference service, data/knowledge pipeline, retrieval, agent tools, evaluation module, APIs, workflows, and monitoring.
Key Responsibilities
- Define component boundaries, intended use, acceptance criteria, non-functional requirements, decision consequences, support expectations, and technical estimates.
- Design and implement maintainable Python/SQL/API/data/model/retrieval/agent-tool/workflow components with contracts, configuration, testing, error handling, and documentation.
- Apply appropriate ML/GenAI techniques (EDA, feature engineering, supervised/unsupervised, baselines, cross-validation, leakage prevention, calibration, subgroup, explainability, error analysis).
- Build GenAI/NLP/RAG/bounded agent components (structured output, embeddings, hybrid search, reranking, provenance/citations, permissions/approvals, retries, recoverable failures).
- Engineer batch/event-driven pipelines with schema validation, lineage/provenance, access control, and consistency checks.
- Define evaluation covering quality, uncertainty, retrieval grounding, citations, task success, tool correctness, safety, latency, cost, and user impact.
- Release/support via cloud, containers, CI/CD, versioning, monitoring, rollback, incident response, and runbooks.
- Apply security, privacy, Responsible AI, validation, auditability, human oversight, and applicable GxP controls; contribute reusable assets and guide associates.
Basic Qualifications
- Master’s degree; or Bachelor’s degree + 2 years CS/IT/related; or Associate’s degree + 6 years; or High school/GED + 8 years.
Preferred Qualifications (select)
- Production AI/ML system design in Python/SQL; APIs/background jobs/event flows; testing/performance/observability.
- GenAI/RAG/agents (structured output, embeddings, hybrid retrieval, reranking, citations, access-aware retrieval, tool schemas, human approval).
- Cloud and MLOps (AWS, Bedrock/SageMaker, Databricks/Spark, Kubernetes, IaC, MLflow, Airflow/Kubeflow/GitHub Actions).
- Advanced ML/deep learning (e.g., PyTorch/TensorFlow, Hugging Face, scikit-learn, uncertainty).
- Regulated enterprise delivery (healthcare/life sciences/GxP).
Benefits
- Total Rewards Plan (eligibility-based): health/welfare, retirement/savings, work/life balance, career development; employee benefits package, discretionary annual bonus (or sales incentive), stock-based long-term incentives, award-winning time-off, and flexible work models where possible.
Application Instructions
- Apply at careers.amgen.com. Applications anticipated until August 31, 2026; sponsorship not guaranteed.
What You Will Do
- Independently own defined production components within enterprise AI products and automation solutions.
- Design, build, release, diagnose, and support components that connect technical measures to user and workflow outcomes.
- Contribute to governed, reusable AI assets across the AI lifecycle (discovery/prototyping to production, reuse, and measurable business impact).
- Components may include: model/inference service, data/knowledge pipeline, retrieval, agent tools, evaluation module, APIs, workflows, and monitoring.
Key Responsibilities
- Define component boundaries, intended use, acceptance criteria, non-functional requirements, decision consequences, support expectations, and technical estimates.
- Design and implement maintainable Python/SQL/API/data/model/retrieval/agent-tool/workflow components with contracts, configuration, testing, error handling, and documentation.
- Apply appropriate ML/GenAI techniques (EDA, feature engineering, supervised/unsupervised, baselines, cross-validation, leakage prevention, calibration, subgroup, explainability, error analysis).
- Build GenAI/NLP/RAG/bounded agent components (structured output, embeddings, hybrid search, reranking, provenance/citations, permissions/approvals, retries, recoverable failures).
- Engineer batch/event-driven pipelines with schema validation, lineage/provenance, access control, and consistency checks.
- Define evaluation covering quality, uncertainty, retrieval grounding, citations, task success, tool correctness, safety, latency, cost, and user impact.
- Release/support via cloud, containers, CI/CD, versioning, monitoring, rollback, incident response, and runbooks.
- Apply security, privacy, Responsible AI, validation, auditability, human oversight, and applicable GxP controls; contribute reusable assets and guide associates.
Basic Qualifications
- Master’s degree; or Bachelor’s degree + 2 years CS/IT/related; or Associate’s degree + 6 years; or High school/GED + 8 years.
Preferred Qualifications (select)
- Production AI/ML system design in Python/SQL; APIs/background jobs/event flows; testing/performance/observability.
- GenAI/RAG/agents (structured output, embeddings, hybrid retrieval, reranking, citations, access-aware retrieval, tool schemas, human approval).
- Cloud and MLOps (AWS, Bedrock/SageMaker, Databricks/Spark, Kubernetes, IaC, MLflow, Airflow/Kubeflow/GitHub Actions).
- Advanced ML/deep learning (e.g., PyTorch/TensorFlow, Hugging Face, scikit-learn, uncertainty).
- Regulated enterprise delivery (healthcare/life sciences/GxP).
Benefits
- Total Rewards Plan (eligibility-based): health/welfare, retirement/savings, work/life balance, career development; employee benefits package, discretionary annual bonus (or sales incentive), stock-based long-term incentives, award-winning time-off, and flexible work models where possible.
Application Instructions
- Apply at careers.amgen.com. Applications anticipated until August 31, 2026; sponsorship not guaranteed.