• Lead a team of engineers across several products and codebases, breaking down work and driving delivery of high-quality, performant solutions. • Own technical direction and standards, driving a strong culture of automated testing, observability, and operational excellence through activities such as hands-on code review. • Address urgent customer needs by triaging and solving Support tickets, balancing short-term fixes with resolving the deeper root causes. • Partner with Product and Design to ensure the team is always working on the most valuable problems, identifying the simplest viable solution to quickly deliver customer impact. • Deliver functionality that is immediately useful, then guide iterative improvements to expand customer value over time. • Identify risks, technical debt, and architectural concerns early, and make pragmatic decisions about when to invest vs. move fast. • Communicate clearly with stakeholders about scope, tradeoffs, timelines, and expectations—from Support issues to new product features. • Present and document significant architectural shifts to Engineering leaders, and regularly communicate performance to ensure system architecture addresses customer and HappyCo needs. • Lead sprint planning, daily technical discussions, and other delivery rituals with the Product Manager and Design Lead.

• Contribute to the development of our open-source AI SDKs, including implementing new features and keeping up-to-date with the latest development practices • Collaborate with our engineering and product teams to drive the implementation and release of major features • Drive key improvements to key developer experience touch points within the SDK • Actively maintain our open-source repositories • Identify areas of improvement and advocate for best practices • Have an active voice in shaping both technology and product decisions

• Design, build, and operate the systems that serve ML models within the messaging stack, with a focus on latency, reliability, and scalability • Write and review technical designs that solve large, open-ended problems at the intersection of ML and product engineering without clearly-known solutions • Partner with ML, data science, and product teams to identify high-value opportunities, establish evaluation criteria, and close the gap between offline model performance and production impact • Collaborate with other engineers and cross-functional partners across Messaging, Trust & Safety, Localization, and Platform organizations to align on long-term technical solutions • Mentor, guide, advocate, and support the career growth of individual contributors • Establish engineering standards for ML integration across the messaging surface, including feature flagging, A/B testing, observability, and graceful degradation

• Developing a highly optimized inference framework running on the world’s largest supercomputers and data centers • Collaborate on innovative, next-generation products at the forefront of technology in terms of performance, scalability, and features