4 days ago
Remote, United States or Thousand Oaks, CA, USAStaff+
Base Salary
$187k - $254k/yr
Responsibilities
- Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud and on-premise environments.
- Build production machine-learning and generative-AI solutions, full-stack AI applications, and end-to-end ML pipelines.
- Deploy, monitor, evaluate, and retrain models while implementing observability, SLOs, drift detection, safe releases, and incident runbooks.
- Architect LLM/RAG systems with prompt management, safety guardrails, and optimized inference.
- Enforce data quality, lineage, model and data cards, and privacy-preserving practices.
- Develop reusable feature stores, model registries, experiment-tracking libraries, and other ML/GenAI components.
- Perform exploratory data analysis, feature ideation, algorithm prototyping, benchmarking, and production-readiness assessment.
- Translate R&D, manufacturing, and commercial needs into technical roadmaps; mentor teams and communicate trade-offs to stakeholders.
Requirements
- Doctorate degree plus 2 years, master's degree plus 6 years, bachelor's degree plus 8 years, associate's degree plus 10 years, or high school diploma/GED plus 12 years of Machine Learning Engineer experience.
- At least 2 years of direct people management or leadership experience leading teams, projects, programs, or resource allocation.
- 3-5 years of AI/ML and enterprise software experience.
- Strong knowledge of machine-learning algorithms, deep-learning architectures, LLM/RAG techniques, and model selection, tuning, and operationalization.
- Experience selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
- Expertise with vector databases, RAG pipelines, prompt-engineering DSLs, and agent frameworks such as LangChain, LangGraph, and Semantic Kernel.
- Proficiency in Python, Java, Docker, Kubernetes, AWS, Azure or GCP, and modern DevOps/MLOps tooling.
- Ability to model TCO versus NPV, present technical trade-offs to executives, manage stakeholders, and communicate complex concepts clearly.
- Preferred qualifications include biotechnology or pharmaceutical experience, published enterprise GenAI thought leadership, a master's degree in Computer Science or Data Science, Agile/SAFe familiarity, and GenAI/ML platform certifications.
- Education and professional certification preferences include a master's degree with 10-12+ years or bachelor's degree with 12-14+ years in Computer Science, IT, or a related field.
Benefits
- Comprehensive employee benefits including retirement and savings contributions, medical, dental, vision, life and disability insurance, and flexible spending accounts.
- Eligibility for a discretionary annual bonus program and stock-based long-term incentives.
- Award-winning time-off plans and career development opportunities.
- Flexible work models where possible, depending on the work location type.
- The role has no stated application deadline and remains open until sufficient candidates are received or a selection is made.
Tech Stack
Categories
About Amgen
Amgen is a public biotechnology company that discovers, develops, manufactures, and sells biologic and biosimilar therapeutics for patients with serious diseases. Its portfolio and pipeline span oncology, cardiovascular disease, osteoporosis, inflammation, and rare diseases, marketed globally through direct sales and partnerships. Founded in 1980 and headquartered in Thousand Oaks, California (NASDAQ: AMGN), Amgen is a component of the Dow Jones Industrial Average and completed the acquisition of Horizon Therapeutics in 2023.