Associate Director, Reinforcement Learning; ML

Associate Director, Reinforcement Learning (ML)

Join Amgen’s mission of serving patients. Since 1980, Amgen has pioneered biotech research, manufacturing, and delivery of innovative medicines across oncology, inflammation, general medicine, and rare disease. Our award‑winning culture is collaborative, innovative, and science based—providing a supportive environment to help you grow while making a lasting impact on patients’ lives.

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 and translate complex concepts into actionable strategies for senior leaders.

• 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 for high‑impact projects, reviewing architectures, experimentation plans, and code, and guiding the team through scientific and engineering trade‑offs.
• Establish best‑practice pipelines for human feedback, partnering with 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 align with strategy, comply with policy, and integrate into real workflows.
• Partner with Data, Platform, and Technology teams to support RLHF workloads with scalable data platforms, model hosting, experimentation infrastructure, and MLOps best practices.
• Champion responsible and compliant AI by 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.

Our ideal candidate brings passion, expertise, and a collaborative mindset to build innovative solutions for real‑world impact. The following qualifications demonstrate your fit:

• Doctorate and 3 years of computer science, IT, or related experience.
• Master’s and 5 years of computer science, IT, or related experience.
• Bachelor’s and 7 years of computer science, IT, or related experience.
• Associate’s and 12 years of computer science, IT, or related experience.
• High school diploma / GED and 14 years of computer science, IT, or related experience.

• Deep, hands‑on expertise in RLHF or advanced reinforcement learning, including reward modeling, policy optimization, exploration strategies, and offline/online evaluation.
• Experience deploying RLHF or RL systems into production for real‑world applications, ideally in healthcare, life sciences, or regulated domains.
• Strong background in modern machine learning and deep learning, with practical experience in Python and frameworks such as PyTorch or Tensor Flow.
• Experience leading cross‑functional initiatives, collaborating with non‑technical stakeholders, and translating needs into impactful AI solutions.
• Excellent communication skills for simplifying complex technical topics to senior executives and non‑technical audiences, including data and model storytelling.
• Experience with large‑scale data and cloud ecosystems (e.g., Azure, Databricks, Snowflake) and partnering with data engineering or platform teams.
• Understanding of responsible AI, safety, and governance, especially for RLHF and LLMs.
• Familiarity with pharma/biotech, healthcare, or regulated industries, including compliance, privacy, and consent practices.
• Strong project management and organizational skills to manage multiple RLHF initiatives in parallel.

The expected annual salary range for this…