Senior Director – Reinforcement Learning
Amgen
full-time
Posted on:
Location Type: Remote
Location: United States
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Job Level
Tech Stack
About the role
- Define enterprise RL roadmaps, landmarks, and success metrics; drive early hands-on prototypes as the capability matures
- Architect simulation environments, reward structures, and training loops for scientific and operational RL use cases
- Lead algorithmic innovation and technical decisions across model-based RL, policy gradient methods, and actor-critic architectures
- Advance RL for scientific domains such as protein design, docking, and structural modeling; expand RL beyond R&D into Manufacturing, Supply Chain, and Commercial applications
- Oversee data pipelines, curation, and feature engineering supporting RL experimentation and multi-modal model training
- Guide RL pilots from proof-of-concept through production deployment, ensuring ML Ops rigor— versioning, automated testing, monitoring, and continuous training
- Partner deeply with biology, engineering, platform teams, product teams, and enterprise AI groups to integrate RL into existing workflows and systems
- Mentor and develop talent; drive innovation, safety, and scientific/engineering excellence
- Evaluate emerging research, open-source frameworks, and frontier methods (e.g., multi-agent RL, RLHF, simulation-based optimization) for enterprise adoption
- Communicate outcomes, technical decisions, and implications to leadership and key stakeholders
Requirements
- Doctorate degree and 5 years of Artificial Intelligence/ Machine Learning experience or Master’s degree and 8 years of Artificial Intelligence/ Machine Learning experience or Bachelor’s degree and 10 years of Artificial Intelligence/ Machine Learning experience
- PhD or equivalent experience in ML, RL, or related fields
- 10+ years AI/ML
- 5+ years reinforcement learning leadership
- Proficient Python
- PyTorch/TensorFlow
- distributed training
- Contributions to AlphaFold-like or large-scale scientific AI
- Publications at NeurIPS , ICML, or ICLR
- Biotech, pharma, or healthcare domain exposure
- Familiarity with GxP , HIPAA, or FDA guidance
- Experience leading AI Centers of Excellence
- Patents or open-source RL contributions
- Prior collaborations with academia or top AI labs
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
- Stock-based long-term incentives
- Award-winning time-off plans
- Flexible work models, including remote and hybrid work arrangements
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
reinforcement learningmachine learningPythonPyTorchTensorFlowdistributed trainingfeature engineeringmodel-based RLpolicy gradient methodsactor-critic architectures
Soft skills
leadershipmentoringcommunicationinnovationcollaborationtechnical decision-makingproject managementproblem-solvingteam developmentstakeholder engagement
Certifications
PhD in ML or RLpublications at NeurIPSpublications at ICMLpublications at ICLRpatents in AIopen-source contributions