This role pays more than
54%
of similar roles. Most pay
$192,050–$246,150
— the shaded band above.
At the midpoint, this role pays about
$220k
versus about
$219k
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
55 open roles
on FindRole.
Listed pay typically runs
$136,906–$185,227
across 47 roles with salary data.
As a Principal Machine Learning Engineer, you will serve as a senior individual-contributor authority focused on building and scaling end-to-end machine learning and generative AI solutions. You will own enterprise AI/ML architecture, standards, and guardrails across cloud and on-premise environments while developing production-grade pipelines for data ingestion, feature engineering, and model evaluation. The role involves building full-stack applications with low-latency insights and managing LLM/RAG architectures with prompt management and safety protocols. You will utilize a technical stack including Python, Java, Docker, Kubernetes, and MLOps tools like Kubeflow, SageMaker Pipelines, and the OpenAI SDK. Key technologies include vector databases, LangChain, and various cloud platforms such as AWS, Azure, or GCP. This role addresses complex data challenges within the biotechnology and pharmaceutical sectors to improve manufacturing, research, and commercial outcomes.
What does a Machine Learning Engineer earn in California?
Median $238250 from 175 postings across 25 companies.
Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud and on-premise environments.
Build and deploy end-to-end ML pipelines including data ingestion, feature engineering, training, and automated promotion.
Develop full-stack AI applications that integrate model services with UI components to deliver sub-second insights.
Establish observability, service level objectives, and safe deployment strategies like blue-green or canary releases.
Architect LLM and RAG systems featuring prompt management, safety guardrails, and optimized inference.
Create reusable ML/GenAI components such as feature stores, model registries, and experiment-tracking libraries.
Perform exploratory data analysis on complex datasets to inform algorithm selection and ensure model robustness.
Translate business requirements from various departments into technical roadmaps and provide mentorship to engineering teams.
What we're looking for
Meet one of the following: Doctorate with 2 years experience, Master’s with 6 years, Bachelor’s with 8 years, Associate’s with 10 years, or High School/GED with 12 years as a Machine Learning Engineer.
Minimum of 2 years of experience managing people or leading teams, projects, programs, or resource allocation.
3 to 5 years of experience in AI/ML and enterprise software.
Expert knowledge of machine learning algorithms, deep learning architectures, and modern LLM/RAG techniques.
Proficiency in Python, Java, containerization (Docker/K8s), cloud platforms (AWS, Azure, or GCP), and MLOps tools.
Expertise in GenAI tooling including vector databases, RAG pipelines, prompt engineering, and agent frameworks like LangChain.
Ability to model business cases such as TCO vs. NPV and communicate complex technical concepts to executives.
Experience in the biotechnology or pharma industry (preferred); Master’s degree in Computer Science or Data Science (preferred).