Associate Director, Reinforcement Learning (ML)

Amgen • Thousand Oaks, CA

2d

About The Position

Join Amgen’s Mission of Serving Patients At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do. Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives. Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career. Associate Director, Reinforcement Learning (ML) What you will do Let’s do this. Let’s change the world. In this vital role you will 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. You will design, implement, and scale RLHF systems to solve real-world problems that ultimately help us serve patients better and faster. This role requires deep technical expertise in RLHF and modern machine learning, combined with strong leadership capabilities in stakeholder management, cross-functional collaboration, and organizational influence. You will be expected to translate complex concepts into clear, actionable strategies for senior leaders and guide teams from idea to impact.

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

Nice To Haves

• 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, including reward modeling, policy optimization, exploration strategies, and offline/online evaluation.
• 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, and familiarity with LLM ecosystems and tooling.
• 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; well-versed in data and model storytelling, including risks, assumptions, and limitations.
• Experience working with large-scale data and cloud ecosystems (e.g., Azure, Databricks, Snowflake, or similar), and partnering with data engineering or platform teams to build robust pipelines and experimentation platforms.
• 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!

Responsibilities

• 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, and helping the team navigate scientific and engineering trade-offs.
• 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.

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, and 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: High school or GED
• Number of Employees: 5,001-10,000 employees