Associate Director, Reinforcement Learning (ML)

• 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

• Doctorate degree and 3 years of Computer Science, IT or related field experience
• Master’s degree and 5 years of Computer Science, IT or related field experience
• Bachelor’s degree and 7 years of Computer Science, IT or related field experience
• Associate’s degree and 12 years of Computer Science, IT or related field experience
• High school diploma / GED and 14 years of Computer Science, IT or related field experience
• 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
• 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

Certifications on Reinforcement Learning (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus

• 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