Associate Director, Reinforcement Learning, ML
Amgen
full-time
Posted on:
Location Type: Remote
Location: Remote • California, Florida • 🇺🇸 United States
Visit company websiteSalary
💰 $NaN per year
Job Level
Senior
Tech Stack
AWSAzureCloudPythonPyTorchTensorflow
About the role
- Lead Amgen’s strategy and execution for Reinforcement Learning from Human Feedback (RLHF) and related reinforcement learning approaches across R&D, medical, operations, and commercial use cases.
- Design, implement, and scale RLHF systems to solve real-world problems that ultimately help us serve patients better and faster.
- Translate complex concepts into clear, actionable strategies for senior leaders and guide teams from idea to impact.
- Lead the design and development of RLHF systems including reward modeling, policy optimization, safety and alignment mechanisms, and evaluation frameworks for large language models and other AI systems.
- Drive hands-on technical execution, particularly for high-impact projects, reviewing architectures, experimentation plans, and code.
- Establish best-practice pipelines for human feedback, partnering closely with internal customer teams to define feedback protocols, annotation quality standards, and governance for RLHF data.
- Define and track success metrics for RLHF systems, balancing offline and online evaluation, A/B tests, safety and robustness criteria, and business or scientific outcomes.
- Collaborate across Amgen leaders to ensure RLHF solutions are aligned with strategy, compliant with policy, and integrated into real workflows.
- Partner with Data, Platform and Technology teams to ensure that RLHF workloads are supported by scalable data platforms, model hosting, experimentation infrastructure, and MLOps best practices.
- Champion responsible and compliant AI, working with Legal, Compliance, and Information Security to implement governance around human feedback, data usage, model behavior, transparency, and risk management in a regulated environment.
- Communicate insights and influence senior stakeholders, creating clear narratives, roadmaps, and recommendations that help executives understand RLHF trade-offs, risks, and opportunities.
Requirements
- Doctorate degree and 3 years of Computer Science, IT or related field experience
- Or Master’s degree and 5 years of Computer Science, IT or related field experience
- Or Bachelor’s degree and 7 years of Computer Science, IT or related field experience
- Or Associate’s degree and 12 years of Computer Science, IT or related field experience
- Or High school diploma / GED and 14 years of Computer Science, IT or related field experience
- Certifications on Reinforcement Learning (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus.
- Deep, hands-on expertise in Reinforcement Learning from Human Feedback (RLHF) and/or advanced reinforcement learning.
- Demonstrated experience deploying RLHF or RL systems into production for real-world applications (e.g., large language models, recommendation systems, decision support tools, or workflow automation), ideally in healthcare, life sciences, or other regulated domains.
- Strong background in modern machine learning and deep learning, with practical experience in Python and frameworks such as PyTorch or TensorFlow.
- Experience driving sophisticated, cross-functional initiatives, collaborating with non-technical stakeholders (e.g., physicians, scientists, commercial leaders, compliance, legal) and translating needs into impactful AI solutions.
- Strong ability to communicate complex technical topics simply, tailoring content to senior executives and non-technical audiences.
- Experience working with large-scale data and cloud ecosystems (e.g., Azure, Databricks, Snowflake, or similar).
- Demonstrated understanding of responsible AI, safety, and governance, especially in the context of RLHF and LLMs (e.g., bias, robustness, transparency, and guardrail design).
- Familiarity with pharma/biotech, healthcare, or other regulated industries, including an understanding of compliance, privacy, and consent practices related to patient and HCP data.
- Strong project management and organizational skills to manage multiple RLHF initiatives in parallel, ensuring work is prioritized against highest-value opportunities and stakeholders are advised on progress and outcomes!
Benefits
- A 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.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
Reinforcement Learning from Human Feedback (RLHF)reward modelingpolicy optimizationsafety mechanismsalignment mechanismsevaluation frameworksPythonPyTorchTensorFlowmachine learning
Soft skills
communicationcollaborationproject managementorganizational skillstranslating complex conceptsinfluencing stakeholderscreating clear narrativesguiding teamsdefining success metricsdriving initiatives
Certifications
Reinforcement Learning certificationAWS AI certificationAzure AI Engineer certificationGoogle Cloud ML certification