Principal Machine Learning Engineer

Amgen is seeking a Principal Machine Learning Engineer for a remote role in the U.S. This is a senior individual-contributor position centered on building and scaling enterprise AI/ML and generative-AI capabilities. The role focuses on owning AI/ML architecture, deploying and monitoring models (including LLMs), and partnering across engineering, security, compliance, DevOps, and product to deliver production-ready solutions that are secure and cost-effective.

Key Responsibilities

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

Requirements

• Education and experience combination: Doctorate degree + 2 years of Machine Learning Engineer experience OR Master’s degree + 6 years OR Bachelor’s degree + 8 years OR Associate’s degree + 10 years OR High school diploma/GED + 12 years.
• At least 2 years of experience directly managing people and/or leadership experience leading teams, projects, programs, or directing allocation of resources.
• 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 the judgment to select, tune, and operationalize approaches.
• Proven ability selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
• Expertise with GenAI tooling including vector databases, RAG pipelines, prompt-engineering DSLs, and agent frameworks such as LangChain, LangGraph, and Semantic Kernel.
• Proficiency in Python and Java, containerization (Docker/Kubernetes), cloud (AWS, Azure, or GCP), and modern DevOps/MLOps such as GitHub Actions and Bedrock/SageMaker Pipelines.
• Strong business-case skills, including ability to model TCO vs. NPV and present trade-offs to executives.
• Exceptional stakeholder management, with ability to translate complex technical concepts into concise, outcome-oriented narratives.

Technologies

• Kubeflow, SageMaker Pipelines, Open AI SDK
• Python, Java
• Docker, Kubernetes (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 the pharma industry
• 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)

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 Details

• Location: Remote (remote)
• Salary: USD 187,395 - 253,534 per year
• Application deadline: Amgen does not have an application deadline for this position and will continue accepting applications until sufficient candidates are received or a selection is made
• Sponsorship: Sponsorship for this role is not guaranteed