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

5+ years of shipping quality software into production while mentoring peer software engineers in a start-up environment
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

Senior Software Engineering Educator – AI Enablement

Key skills