• Lead and grow a team of ML engineers focused on production ML systems • Lead model improvements in response to production issues, product feedback, and new research or platform advancements • Lead production release processes for ML services, including release planning, CI/CD, staged rollouts, and rollback procedures • Build and operate observability and on-call practices for ML features, including monitoring, alerting, dashboards, incident response, and post-incident reviews • Develop and maintain scalable evaluation frameworks, datasets, and automated regression tests to prevent quality regressions • Lead reliability, performance, and cost improvements for inference and serving, including capacity planning and meeting SLAs (latency, throughput, availability) • Partner with researchers, product, and platform teams to define quality bars and production readiness, including Trusted AI requirements • Establish and evolve production standards and governance across ML features (testing, evaluation methodology, release gates, model versioning and lineage) • Partner with platform and product teams to integrate ML capabilities into products

• Manage, support, and mentor a diverse group of engineers • Give engineers the tools, confidence, and motivation to make choices that result in recognition for themselves and not just for yourself • Remove roadblocks for your team and escalate when you need additional context or direction • Drive your team toward their career goals, identify milestones, and set them up for success along the way • Promote a culture of feedback and transparent communication • Partner with internal stakeholders on feature definition and drives breakdown of work to support organization objectives • Partner closely with the management team, HR, and Recruiting to attract, recruit, and develop a diverse team of high-quality engineers • Proactively maintain alignment between business goals and objectives with the team's day-to-day work • Work to resolve or escalate issues that serve as roadblocks and place deliverables and goals at risk • Gain and maintain visibility into trade-offs between options and priorities to positively influence the approach taken by the team • Design feedback loops to incorporate members' feedback into continuous improvement activities

• Lead two high-impact mobile pods: Mobile Platform and Mobile Product • Define and evolve cross-platform strategy • Improve build systems and developer productivity • Establish performance SLOs • Drive architectural simplification and introduce AI-assisted SDLC tooling • Lead customer-facing feature development • Partner closely with Product & Design • Manage ~13-14 engineers across 2 pods • Hire and retain high-performing mobile engineers • Establish measurable KPIs and ensure predictable sprint delivery

• Define and evolve cross-platform strategy (Native vs React Native vs Compose/KMP) • Improve build systems, CI times, and developer productivity • Establish performance SLOs (launch time, page load, memory, crash-free sessions) • Drive architectural simplification and reduce tech fragmentation • Introduce AI-assisted SDLC tooling where impactful • Ensure mobile scalability for IoT-heavy, real-time use cases • Lead customer-facing feature development (e.g., messaging, fleet workflows) • Partner closely with Product & Design to deliver high-quality mobile experiences • Balance feature velocity with platform health • Drive disciplined engineering practices (PR reviews, testing, observability) • Manage ~13-14 engineers across 2 pods • Hire and retain high-performing mobile engineers • Develop senior ICs and future tech leads • Build a culture of ownership, accountability, and technical rigor • Coach engineers to improve system design and execution skills • Establish measurable KPIs: Build time; CI stability; Crash-free rate; App startup latency; Feature cycle time • Ensure predictable sprint delivery • Improve incident response and reliability practices • Maintain high engineering quality standards • Debate architecture credibly with Staff engineers • Know when to invest in platform vs product • Lead large migrations or platform rebuilds • Understand both developer experience and customer experience deeply