below marketmost similar roles pay hereabove market
This role pays less than
53%
of similar roles. Most pay
$202,800–$254,750
— the blue band above.
At the midpoint, this role pays about
$220k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Amgen
Amgen is a leading biotechnology company that discovers, develops, manufactures, and delivers innovative human therapeutics, with products addressing conditions such as anemia, bone loss, inflammation, and cancer. Industry: Biotechnology & Pharmaceuticals
Amgen currently has
66 open roles
on FindRole.
Listed pay typically runs
$156,190–$211,315
across 45 roles with salary data.
The Principal Machine Learning Engineer joins the AI & Data Innovation Lab to lead initiatives from initial business ideas to validated proof of concepts for enterprise adoption. This role involves partnering with business leaders to define problems, assessing feasibility, and designing AI solutions that demonstrate measurable outcomes. You will architect and build proof of concepts using LLMs, agentic workflows, retrieval-augmented generation, data pipelines, APIs, and cloud-native services. Key responsibilities include addressing data readiness, security, compliance, and performance while establishing reusable patterns and mentoring other engineers. Required skills include Python, full-stack development, AWS, Terraform, and CI/CD. You will solve technical challenges by turning ambiguous opportunities into well-scoped projects, ensuring that innovative AI methods are integrated responsibly into the enterprise technical foundation.
What does a Machine Learning Engineer earn in California?
Median $231150 from 191 postings across 27 companies.
Lead AI initiatives from initial business concepts to validated proofs of concept for enterprise adoption.
Design and build AI solutions using LLMs, agentic workflows, RAG, and cloud-native services.
Architect scalable systems and create reusable components for product and platform teams to implement.
Evaluate feasibility and business value by assessing data readiness, security, performance, and operational costs.
Define technical requirements and success measures for cross-functional teams to turn ambiguous opportunities into scoped projects.
Provide technical leadership and mentorship to guide engineering design decisions and delivery practices.
Establish engineering standards including documentation, decision records, and handoff materials for production scaling.
What we're looking for
Must have a Doctorate with 2 years of Machine Learning Engineer experience, a Master's with 6 years, a Bachelor's with 8 years, or an Associate's with 10 years.
Must have a High school diploma or GED and 12 years of Machine Learning Engineer experience.
Substantial experience in machine learning and software engineering, including senior technical ownership of applications beyond prototype (preferred).
Deep full-stack development skills including Python, APIs, application back ends, user interfaces, and modern software design (preferred).
Hands-on experience with cloud platforms like AWS and DevOps practices including Terraform, containers, CI/CD, and observability (preferred).
Strong practical knowledge of generative AI, model selection, evaluation, inference, and trade-offs between managed services and open-source tools (preferred).
Experience with enterprise architecture, secure data handling, reliability, performance, and establishing AI evaluation approaches (preferred).
Demonstrated technical leadership through architecture decisions, mentorship, and at least 2 years of experience leading teams or programs (preferred).