• Lead the technical design and implementation of AI-powered product features from concept through production. • Own end-to-end architecture for AI solutions, including data flows, model integration, APIs, and application integration. • Make pragmatic decisions to accelerate delivery while maintaining system integrity. • Ensure AI solutions are secure, observable, scalable, and aligned with platform standards. • Act as the technical lead for a cross-functional AI Pod. • Break down product requirements into executable technical workstreams and prototypes. • Guide rapid iteration cycles, proofs-of-concept, and MVPs, balancing experimentation with production readiness. • Review code, architecture, and technical decisions to maintain quality and velocity. • Partner closely with Product Management to shape problem definitions, success metrics, and delivery plans. • Collaborate with the Data Science Leader to integrate models, analytics, and data assets into product workflows.

• Strong sense of product ownership and actively seek responsibility • Work in an autonomous environment on a close-knit team • Partner closely with designers and product leaders to ship lovable experiences • Join a team of experienced product engineers within one of our Mercury product groups

• Feel a strong sense of product ownership and actively seek responsibility • Work in an autonomous environment on a close-knit team • Partner closely with designers and product leaders to ship lovable experiences • Join a small but mighty team of experienced product engineers

• 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