Build the platform that helps every engineering team work better Help shape technical direction. Contribute to the platform roadmap by finding gaps, proposing architectural improvements, and linking your designs to the most important needs of engineering teams. Apply AI to real engineering problems. Build AI-assisted and agent-based features for workflows like troubleshooting, testing, migrations, and operational analysis. Add evaluations and safeguards so engineers can trust the results. Treat the platform as a product. Work with partner teams to understand where they get stuck, test solutions with them, and turn common problems into self-service features that engineers want to use. Make safe delivery easier. Build automated validation, progressive rollout, observability, and recovery tools that let teams move fast without adding risk to production. Own your work end to end. Deliver significant platform features from design through implementation, adoption, and ongoing operation. Measure success by developer experience, reliability, performance, and cost. Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Master'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 OR Bachelor's Degree in Computer Science or related technical field AND 8+ years 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, and operating distributed systems or shared platform services in production. Experience building platform components or developer tools used by multiple teams that improved engineering productivity, service reliability, or operational efficiency. Hands-on experience integrating large language models or agents into engineering workflows, including tool integration, evaluation, permission boundaries, and human oversight. Experience building interfaces and workflows that work for both human developers and AI agents, with clear contracts, discoverable capabilities, and auditable execution. Depth in at least one platform area, such as service frameworks, data infrastructure, observability, CI/CD, experimentation, configuration management, or cloud infrastructure. Experience using telemetry, profiling, and production investigations to find bottlenecks and deliver measurable performance or cost improvements. Experience modernizing existing systems through incremental changes and migrations without disrupting customers or partners. A record of balancing exploration with practical delivery, turning promising prototypes into supported, production-ready platform features. Solid systems engineering fundamentals, including API design, concurrency, data management, failure handling, and performance analysis. An AI-forward engineering mindset: the ability to spot useful ways to apply AI, build them into production code, and judge how correct, secure, reliable, and cost-effective they are. A record of delivering complex technical projects from design through production, including making sound design decisions and working with other teams to get features adopted. Ability to turn ambiguous requirements and developer needs into practical, maintainable technical solutions. Solid communication and collaboration skills, including explaining technical tradeoffs clearly, contributing to design discussions, and helping other engineers grow.