• Own complex features and subsystems in industry-leading voter/donor management platforms • Combine deep technical depth with systems thinking, identifying technical debt, proposing architectural improvements, and mentoring peers across engineering and QA teams • Participate in system design decisions for complex features • Propose approaches that balance performance, maintainability, scalability, and business needs • Mentor junior and peer engineers through code reviews and design collaboration • Model best practices and drive technical excellence • Design and implement complex features for voter/donor management, third-party integrations, and AI-powered enhancements • Deliver high-quality, maintainable code that meets performance, security, and scalability standards • Proactively identify anomalies, inefficiencies, and technical gaps • Propose and champion improvements that enhance product health.

• Technical and people leadership • Provide technical leadership to the team — defining standards, technical direction, and a code review culture — and mentor team members' growth. • Drive architectural decisions in the AWS environment, evaluating scalability and performance trade-offs. • Manage, coordinate and execute activities. • Participate in business refinement sessions, assessing the technical feasibility of Product team stories and suggesting directions. • Prepare and technically refine requirements and allocate developers to tasks. • Ensure execution of the development → code review → staging/QA → deploy flow. • Develop applications with TypeScript, Node.js (NestJS and Express) and React. • Design and implement the platform’s distributed architecture — microservices, messaging, caching, Pub/Sub, APIs and jobs/cronjobs. • Keep up with the evolution of the ecosystem (including AI) and propose adoptions that deliver real product value.

• Work within existing Node.js, Next.js, and React applications • Develop, improve, and maintain product features based on defined requirements • Help move existing products toward production-ready quality • Troubleshoot, debug, and resolve application issues • Collaborate with the team while working independently on assigned tasks • Utilize AI-assisted development tools (e.g. Claude Code) as part of the development workflow • Write clean, maintainable, and well-structured code • Participate in code reviews and technical discussions when needed

• Define the architecture and contracts governing how models move from development to production — feature store design, model schema management, online/offline inference consistency, and multi-version support. • Lead the buildout of a unified serving stack that eliminates per-model one-off implementations and gives data scientists a turnkey path from training to production. • Architect backfill and evaluation infrastructure so the modeling team can simulate production inference over historical data in days, not weeks. • Establish domain contracts between Modeling and Serving so each team can move independently with clear, enforced interfaces. • Review and evolve the ML serving architecture — making tradeoff calls on feature pipeline design, model composition, and API interfaces. • Write and review code for feature engineering jobs, feature store configurations, and serving service endpoints. • Partner with Data Science, MLE, MLI and core Pricing & Availability systems BE teams to define artifact handoffs and integration contracts. • Drive milestone planning across the Host Pricing & Settings org, sequencing work to deliver value incrementally. • Mentor engineers through design reviews and hands-on pairing on the hardest infrastructure problems.