Senior Software Engineering Educator - AI Enablement

Mercury is a fintech company focused on transforming how software engineers leverage AI in their workflows. The role involves designing and implementing training programs that help engineers effectively use AI tools while maintaining essential coding skills and craftsmanship.

Responsibilities:

• Define and operationalize Mercury's AI-usage guidelines for engineering: what engineers should use AI for, what they shouldn't, and how those boundaries shift as skill and context deepen
• Design structured checkpoints and assessment frameworks that detect when AI reliance is accelerating growth versus eroding foundational skills like debugging, code reading, and system reasoning
• Create clear 'when to unlatch AI' triggers for onboarding and training—criteria that tell engineers and their managers when someone has built enough foundation to lean more heavily on AI tooling
• Build and iterate on AI-aware training materials that model the right balance: hand-crafted coding where it builds understanding, AI-assisted workflows where it multiplies leverage
• Partner with managers and lead engineers across experience levels to embed AI-usage norms into 1:1s, growth conversations, and performance discussions—not as a separate initiative, but as part of how Mercury engineers develop
• Stand up and evolve a mentorship-focused initiative for software engineers, ensuring mentors model thoughtful AI usage alongside strong engineering craft
• Do the operational work that drives adoption: scheduling, facilitation, follow-ups, and iteration based on feedback
• Collaborate closely with training team members and cross-functional partners to drive broader skill acquisition efforts

Requirements:

• Has 5+ years of shipping quality software into production while mentoring peer software engineers in a start-up environment
• Has hands-on experience with AI coding tools and a thoughtful, opinionated perspective on where they help and where they hinder
• Communicates clearly and gives actionable, direct, kind feedback
• Enjoys turning fuzzy goals into simple, repeatable programs
• Knows when to lean into 1:1 sessions or organizational legwork to drive adoption or improve learning outcomes
• Models a care of craftsmanship and healthy engineering habits—including deliberate, principled AI usage rather than reflexive reliance
• Loves turning passive, explanatory content into active, exercise-centric learning resources
• Is energized by the tension between productivity and skill development, and sees designing for both as a core challenge rather than a tradeoff to accept
• Haskell experience or strong willingness to learn Mercury's primary stack is a plus