Job Opportunities API

Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The company’s R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins. The company’s RAS(ON) inhibitors daraxonrasib (RMC-6236), a RAS(ON) multi-selective inhibitor; elironrasib (RMC-6291), a RAS(ON) G12C-selective inhibitor; zoldonrasib (RMC-9805), a RAS(ON) G12D-selective inhibitor; and RMC-5127, a RAS(ON) G12V-selective inhibitor, are currently in clinical development. As a new member of the Revolution Medicines team, you will join other outstanding professionals in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway. The Opportunity: We are seeking a Manager, AI Learning Enablement & Coaching to drive practical adoption of approved AI tools across the enterprise through hands-on coaching, small-group and use-case-based learning. This role will translate established AI enablement strategy into applied learning experiences that help employees use AI tools effectively, responsibly, and confidently in day-to-day work.  This is an execution-focused role centered on behavior change and applied capability building. The role will help employees understand where AI can create value, how to validate outputs, how to use approved tools appropriately in a regulated environment, and how to apply AI to real work scenarios across functions.  Key Responsibilities  AI Coaching and Applied Enablement  Deliver small-group coaching sessions, office hours, practical learning labs, and role-based working sessions focused on approved AI tools.   Coach employees on applying AI to real work scenarios such as drafting, summarization, research support, analysis, ideation, workflow improvement, and productivity use cases.   Teach employees how to assess where AI adds value, when human judgment is required, and how to validate AI-generated outputs before use.   Reinforce responsible-use expectations for AI in a regulated environment, including privacy, sensitivity, and appropriate use boundaries.   Adapt sessions by role, function, skill level, and confidence level to improve relevance and practical uptake.   Use-Case-Based Learning  Translate real employee needs, coaching themes, and Catalyst Network insights into practical learning experiences.   Gather and refine examples of approved AI use across functions and convert them into reusable demonstrations, exercises, and guided practice.   Help employees connect enterprise use cases to their own workflows and identify immediate next-step applications.   Learning Content and Resource Curation  Curate practical learning resources such as prompt examples, job aids, quick-reference guides, FAQs, recordings, and self-paced materials based on employee demand, define and track effectiveness measures.   Identify recurring learning gaps and recommend or develop targeted materials to address them.   Partner with the Knowledge Management & Technology Enablement role to ensure content remains organized, current, discoverable, and governed.   Feedback and Continuous Improvement  Capture feedback from sessions, office hours, surveys, and learner interactions to identify barriers, priority topics, and common questions.   Use feedback to improve coaching formats, content relevance, and training priorities.   Share recurring themes and recommendations with Digital & AI Enablement leadership to inform ongoing program refinement.   Program Execution  Execute AI learning activities in