Build and own scalable distributed backend services powering experiences such as Critique and Council, remaining hands-on from design and implementation through shipping and production operation. Define rigorous evaluation methodologies, offline benchmarks, and online experimentation frameworks to measure model and product performance, establish a high bar for quality, and guide data-driven iteration and release decisions. Turn emerging AI capabilities into meaningful customer experiences, partnering across disciplines to clarify requirements, navigate ambiguity, and make sound technical tradeoffs. Lead complex initiatives across teams and organizations, aligning stakeholders, defining interfaces and resolving dependencies, and balancing competing priorities. Bring engineering best practices through mentoring, architecture and code reviews, reusable patterns, and maintainable software. Champion responsible AI-assisted and agentic engineering, to improve dev agility, quality, learning, and speed. Bachelor's degree in computer science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python These requirements include but are not limited to the following specialized security screenings: Master's degree in computer science or related technical field AND 6+ years or bachelor's degree in computer science AND 8+ years of technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience designing, building, shipping, and operating large-scale distributed systems, cloud services, or complex production platforms. Experience building production AI systems using large language models, retrieval-augmented generation, agent orchestration, tool integration, or model gateways. Experience with AI evaluation and prompt management for production applications. Demonstrated ability to lead complex technical initiatives, influence architectural decisions, drive engineering standards, and align teams across organizational boundaries through clear written and verbal communication. Knowledge of Responsible AI and AI safety practices, including privacy, security, compliance, and data-governance requirements for enterprise AI systems. Experience with modern AI and agentic engineering practices, including workflow automation and AI-assisted development.