Principal Machine Learning Engineer (Reinforcement Learning)
About this opportunity
Career Category
Information Systems
Job Description
Job Description
ABOUT AMGEN
Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today.
ABOUT THE ROLE
Role Summary
Principal-level AIML Engineer responsible for designing and deploying advanced AI/ML systems with a strong focus on Reinforcement Learning (RL) and decision intelligence. Will act as a technical leader driving scalable AI solutions from research to production across enterprise platforms.
Key Responsibilities
Design and develop Reinforcement Learning models (RL, RLHF, multi-agent RL) for real-world decision-making problems
Build and deploy scalable ML pipelines and production AI systems using MLOps best practices
Architect end-to-end AI systems integrating RL with GenAI, LLMs, or agent-based frameworks
Lead development of agent-based / multi-agent AI systems for planning, reasoning, and automation
Translate research concepts into production-grade, reliable ML systems
Partner with data scientists, engineers, and product teams to deliver enterprise AI solutions
Evaluate new AI techniques (RLHF, agentic systems, deep RL) and drive adoption
Mentor engineers and provide technical leadership and architectural guidance
Must-Have Skills
Strong expertise in Reinforcement Learning (Deep RL, Policy Optimization, RLHF)
Hands-on experience building production AI/ML systems at scale
Strong programming in Python
Experience with MLOps (MLflow, Kubeflow, SageMaker, etc.)
Knowledge of Distributed Systems & Cloud (AWS/Azure/GCP)
Experience in model deployment, monitoring, and lifecycle management
Strong understanding of ML/DL algorithms and optimization techniques
Good-to-Have
Experience with multi-agent systems / agentic AI frameworks
Exposure to LLMs, RAG, or Generative AI systems
Experience with simulation environments (Gym, RLlib, etc.)
Background in optimization, control systems, or operations research
Qualifications
Bachelor’s or Master’s in Computer Science, AI, ML, or related field
12 to 17 years experience in ML/AI engineering or related domains
Behavioral / Leadership Expectations
Strong technical leadership and mentoring capability
Ability to translate complex AI concepts into business impact
Ownership mindset with end-to-end delivery focus
Collaboration across global teams
EQUAL OPPORTUNITY STATEMENT
Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.
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Job details
How this role compares
Computed from every other active Information Technology role in our database, not just this employer's listings.
We currently track 582 comparable Principal Information Technology roles across 54 biopharma companies.
Salary context
102 of 582 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 582 comparable roles
+ 25 more countries
Seniority mix
582 of 582 peers have a known seniority level
Therapeutic area mix
5 of 582 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.