• Architect and implement new features based on customer requirements and UX design specifications • Address customer technical issues, data fixes, and production bugs as they arise • Contribute to the continuous improvement of existing code and features • Provide support to the QA team as needed • Participate in regular stand-ups and an agile work environment

• Build a system that ensures micro-services are secure, performant, reliable, and can go from idea to production in an hour. • Build a system that continuously provides recommendations to right-size computing resources for Kubernetes to ensure efficient cloud spending for ourselves and our customers. • Design and architect automated quality platforms to go from Enterprise-Grade releases from once-a-quarter to once-a-week to once-per-day to once-per-hour without sacrificing performance, security, or reliability • Work with Product, Legal and Security to ensure the continuous delivery processes you build are compliant and secure • Work with the team to ensure pipelines have clear playbooks and can operate 24/7 without you • Work with a diverse group of architects and platform engineers across our R&D department to set continuous delivery and performance requirements for all production services • Work with internal product managers to set roadmaps and define milestones to deliver innovative and simple solutions to our many teams’ continuous delivery and platform engineering issues

• Own the end-to-end lifecycle of production issues, including triage, investigation, incident response, postmortems, and follow-up actions. • Troubleshoot complex, cross-system issues, identify root causes, and implement long-term fixes. • Design, implement, and maintain monitoring, alerting, and dashboards to proactively detect reliability and performance issues. • Use AI-assisted tools responsibly to accelerate debugging, log analysis, incident response, and knowledge sharing. • Partner with Product, Engineering, and Customer Success to resolve customer-impacting issues efficiently and transparently. • Reduce recurring operational issues through automation, improved tooling, and process improvements. • Contribute code to improve reliability, observability, scalability, and operational safety. • Document incidents and standard operating procedures to improve response consistency and team effectiveness.

• Prototype and evaluate fleet optimization algorithms for problems like vehicle rebalancing, charging strategies, and maintenance/cleaning scheduling. • Explore reinforcement learning–based approaches for selected dispatch decisions. • Design and run simulation experiments to compare algorithm variants using various metrics. • Contribute production-quality code to the Dispatch codebase in C++ and/or Python. • Collaborate with teammates to translate high-level operational or commercial questions into well-posed optimization or simulation studies. • Work with other autonomy and platform teams to understand constraints and incorporate them into models and algorithms. • Participate in code reviews and design discussions.