Principal Machine Learning Engineer

Join Amgen as a Principal Machine Learning Engineer in a senior individual-contributor role focused on enterprise AI/ML and generative-AI solutions. This position helps shape AI/ML architecture and standards, deploys and monitors models including LLMs, and partners across DevOps, Security, Compliance, and Product to deliver secure, cost-effective, production-ready outcomes. The role is remote in the U.S. and supports flexible work models where possible, with a comprehensive employee benefits package including company contributions to a Retirement and Savings Plan.

Responsibilities

• Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud and on-prem environments.
• Build production ML and GenAI solutions, including lightweight apps that deliver sub-second insights.
• Design end-to-end ML pipelines for ingestion, feature engineering, training, hyper-parameter optimization, evaluation, registration, and automated promotion using Kubeflow, SageMaker Pipelines, Open AI SDK, or equivalent MLOps stacks.
• Create and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines, or business-logic layers to reach users with sub-second latency.
• Establish observability, define SLOs, and implement safe deployments (blue-green/canary, shadow, rollbacks) with incident runbooks.
• Lead rigorous evaluation (offline and online, including A/B testing), drift detection, and automated retraining.
• Architect LLM and RAG solutions, including prompt management, safety guardrails, and optimized inference.
• Enforce data quality, lineage, and model/data cards, applying privacy-preserving techniques when needed.
• Develop reusable ML and GenAI components (feature stores, model registries, experiment-tracking libraries) and help raise engineering velocity across squads through best-practice evangelism.
• Conduct exploratory data analysis and feature ideation on complex, high-dimensional datasets to support algorithm selection and model robustness.
• Prototype and benchmark new algorithms, guiding production-readiness and scalability trade-offs while co-owning model-performance KPIs.
• Translate domain needs from R&D, Manufacturing, and Commercial teams into roadmaps, mentor teams, and communicate trade-offs.

Requirements

• Education and experience: Doctorate degree with 2+ years of Machine Learning Engineer experience OR Master’s degree with 6+ years OR Bachelor’s degree with 8+ years OR Associate’s degree with 10+ years OR High school diploma/GED with 12+ years.
• Minimum of 2 years experience directly managing people and/or leadership experience leading teams, projects, programs, or directing resource allocation.
• 3–5 years in AI/ML and enterprise software.
• Strong command of machine-learning algorithms (regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures including CNNs, RNNs, transformers) and modern LLM/RAG techniques, with judgment to choose, tune, and operationalize appropriate methods for business problems.
• Proven ability selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
• Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs, and agent frameworks such as LangChain, LangGraph, and Semantic Kernel.
• Proficiency in Python and Java, with containerization (Docker/K8s) and cloud experience (AWS, Azure, or GCP) plus modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines).
• Strong business-case skills, including modeling TCO vs. NPV and presenting trade-offs to executives.
• Exceptional stakeholder management, translating technical concepts into concise, outcome-oriented narratives.

Technologies

Kubeflow, SageMaker Pipelines, Open AI SDK, Python, Java, Docker, K8s, AWS, Azure, GCP, GitHub Actions, Bedrock, vector databases, LangChain, LangGraph, Semantic Kernel, LLM/RAG, prompt management, blue-green/canary, shadow, rollbacks.

Benefits

• Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions.
• Group medical, dental, and vision coverage.
• Life and disability insurance.
• 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.

Preferred Qualifications

• Experience in Biotechnology or pharma industry is a big plus.
• Published thought-leadership or conference talks on enterprise GenAI adoption.
• Master’s degree in Computer Science and or Data Science.
• Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.

Education and Professional Certifications

• Master’s degree with 10–12+ years of experience in Computer Science, IT, or related field.
• Bachelor’s degree with 12–14+ years of experience in Computer Science, IT, or related field.
• Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus.

Additional Notes

• Application deadline: Amgen does not have an application deadline for this position and will continue accepting applications until a sufficient number are received or a candidate is selected.
• Sponsorship: Sponsorship for this role is not guaranteed.