• Lead and grow a senior team of frontend platform engineers supporting Ambient AI • Set clear technical and execution standards in a high-bar, healthcare-grade environment • Coach engineers on scope ownership, technical decision-making, and cross-team influence • Hire and develop engineers who can operate across platform, product, and design boundaries • Own planning, execution, and delivery for platform initiatives with clear product impact • Own the frontend platform used across Ambient experiences (web, mobile, embedded surfaces) • Lead architecture for real-time and async workflows (audio capture, streaming updates, AI state transitions) • Build and scale shared UI primitives, component libraries, and design systems used across Ambient • Improve frontend performance, latency, reliability, and offline/poor-network behavior • Establish patterns for error handling, loading states, partial data, and confidence signaling in AI workflows • Create strong frontend abstractions that reduce duplication across Ambient product teams • Improve local dev, testing, CI/CD, and release workflows for frontend engineers • Define standards for frontend observability, performance metrics, and user-impact monitoring • Partner with backend and ML teams to define clean contracts between AI systems and UI layers • Partner deeply with Product to translate clinical workflows into scalable UI platforms • Work closely with Design to evolve clinician-grade design systems and accessibility standards • Collaborate with Compliance and Security to ensure frontend patterns meet healthcare requirements • Influence frontend technical direction across the broader engineering organization • Drive architectural reviews and RFCs for major Ambient frontend initiatives • Help teams navigate tradeoffs between speed, quality, and safety in AI-driven UX • Stay close enough to the code and architecture to unblock complex problems when needed • Ensure platform decisions anticipate future scale (new care settings, modalities, and products

• Leading and growing a high‑performing engineering team • Owning execution for Ecosystems services and integrations • Guide your team through key architectural decisions across distributed systems, public APIs, webhooks, and integration patterns including agentic protocols like MCP. • Raise the bar on reliability, scalability, and performance, including SLIs/SLOs, observability, testing strategy, and incident readiness for the services you own. • Partner with other Platform and product squads to ensure Calendly experiences are API‑first and ecosystem‑ready. • Lead and refine agile practices to help your team deliver predictably and sustainably. • Provide feedback and guidance to help us continuously grow, scale, and evolve our operating model across the broader Engineering org.

• Own the end‑to‑end strategy, roadmap, and execution for Instacart’s managed database platform • Lead and develop a high‑performing team of 5 engineers • Design and operate multi‑tenant database offerings with strong availability • Build automation and control planes for provisioning, schema and version management • Partner with Security, SRE, Data Platform, and product engineering to migrate legacy systems • Deliver meaningful cost outcomes by optimizing instance sizing and performance • Establish best practices in observability, testing, CI/CD, change management

• Set the vision and strategy for all aspects of model inference and serving at Netflix, ensuring our platform supports the next generation of ML innovation, including LLMs, GenAI, and real-time personalization. • Lead and develop a cohort of engineering managers and technical leads responsible for core functions, including model routing, inference systems, experimentation, serving frameworks, and the performance and scalability of model serving at Netflix. • Drive cross-team and cross-functional alignment, collaborating with ML researchers, product engineering, infrastructure, and platform partners to maximize business and member impact. • Champion operational excellence and continuous improvement, ensuring reliability, scalability, and cost-effectiveness across all model serving systems. • Define and communicate the pillar’s multi-year vision, technical strategy, and roadmap. • Anticipate future platform and business needs, especially as ML architectures and use cases evolve. • Drive the transition from legacy, domain-based serving to a unified, modular, and domain-agnostic serving platform. • Manage and mentor engineering managers and technical leads; build a strong leadership bench. • Foster a culture of high performance, candor, innovation, and inclusion, aligned with Netflix’s values. • Attract, hire, and retain outstanding talent across the pillar. • Set and uphold technical standards for reliability, scalability, and performance across all teams. • Oversee development of foundational serving infrastructure: real-time/batch inference, frameworks, experimentation, control plane, and tooling. • Ensure robust support for diverse model types (deep learning, LLMs, bandits, etc.), hardware targets (CPU/GPU), and SLAs. • Own operational health and reliability at scale, including observability, SLOs, and incident response. • Build and maintain strong partnerships with ML practitioners, product engineering, infrastructure, and platform teams. • Represent the Model Serving pillar to Netflix senior leadership, clearly communicating the vision, progress, and priorities. • Influence and drive alignment on platform direction, investment, and priorities.