Machine Learning Engineer, AI Studio at Amgen

Job description

Career Category Information Systems 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. Machine Learning Engineer, AI Studio What you will do The Machine Learning Engineer 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 independently own defined production components within enterprise AI products and automation solutions. Your remit may include a model or inference service, data or knowledge pipeline, retrieval component, agent tool, evaluation module, API, workflow or monitoring capability. 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. Key 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. 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. Basic Qualifications: 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 Preferred 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. What you can expect of us 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. Apply now and make a lasting impact with the Amgen team. careers.amgen.com In any materials you submit, you may redact or remove age-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information. Application deadline Amgen anticipates accepting applications until August 31st, 2026; however, we may continue accepting applications beyond this date if we do not receive a sufficient number of candidates. Sponsorship Sponsorship for this role is not guaranteed. As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease. Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. . Salary Range 129,654.75USD -175,415.25 USD

Requirements

Must-haves

Career Category Information Systems 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.
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

Nice-to-haves

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.

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machine learningaiapipythonsqlci/cdmlawskubernetes

Platform: workday
Effort: long

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