Director, Reinforcement Learning, RLHF

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Amgen

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Biotechnology • Pharmaceuticals • Science

Amgen is a global leader in biotechnology, focusing on the development and commercialization of innovative medicines made from living cells. The company aims to treat serious illnesses, often targeting diseases with limited therapeutic options. Amgen emphasizes scientific innovation and is committed to ethical research, patient safety, and environmental sustainability. It actively engages in clinical trials and is known for its contributions to the fields of cancer treatment and obesity management among others.

10,000+ employees

Founded 1980

🧬 Biotechnology

💊 Pharmaceuticals

🔬 Science

💰 $28.5G Post-IPO Debt on 2022-12

📋 Description

• Lead Amgen’s strategy and execution for Reinforcement Learning from Human Feedback (RLHF) • Build and lead a high-performing, hands-on technical team that designs, implements, and scales RLHF systems • Translate complex concepts into clear, actionable strategies for senior leaders • Set the RLHF vision and roadmap for Amgen, identifying high-impact opportunities • Lead the design and development of RLHF systems including reward modeling, policy optimization, safety and alignment mechanisms • Drive hands-on technical execution for high-impact projects • Establish best-practice pipelines for human feedback • Define and monitor success metrics for RLHF systems • Collaborate across Amgen leaders to ensure RLHF solutions are aligned with strategy • Partner with Data, Platform and Technology teams to support RLHF workloads • Champion responsible and compliant AI, working with Legal and Compliance • Communicate insights and influence senior stakeholders.

🎯 Requirements

• Master’s degree with 10-12 + years of experience in Computer Science, IT or related field • Or Bachelor’s degree with 12-14 + years of experience in Computer Science, IT or related field • 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. • Consistent track record of leading technical teams—recruiting, developing, and coaching machine learning scientists and engineers, and creating an inclusive, high-performance culture. • Experience driving complex, 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, clarity, 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 handle multiple RLHF initiatives in parallel, ensuring work is prioritized against highest-value opportunities and interested parties are informed 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.