- Design, build, and operate production-grade platform services and infrastructure with strong reliability, observability, and performance.
- Identify manual, repetitive engineering workflows and eliminate them through self-service automation on the platform.
- Build and evolve the agentic layer: agent lifecycle, tool/MCP integrations, sandboxed execution, multi-agent workflows.
- Design the semantic data layer that grounds agents: model our systems, workflows, and decision data as connected objects and tools that agents can query and act on.
- Build safe action execution paths: connectors to internal and operational systems, staged propose–validate–approve–commit flows, with granular access control and end-to-end decision lineage/audit.
- Own system design for new platform capabilities end to end, from requirements through rollout and operations.
- Work directly with customers: lead technical onboarding, gather requirements, debug production issues, and turn field feedback into roadmap priorities.
- Establish best practices for agent evaluation, observability, and safety guardrails.
- Master's degree in Computer Science, Software Engineering, or related fields.
- 5+ years building and operating production distributed systems, with demonstrated strength in system design and software engineering fundamentals.
- Proficiency in Python and TypeScript; solid grasp of databases, message queues, caching, and API design.
- A builder's instinct for automation: a track record of turning manual processes into tools, pipelines, or self-service systems.
- Hands-on experience with modern AI coding tooling and LLM APIs, including tool use/function calling.
- Experience with agentic systems or task orchestration platforms (workflow engines, multi-agent frameworks, MCP servers, or similar).
- Strong cross-team communication skills.
- Familiarity with Autonomous Driving terminologies and model development workflows.
- Direct customer-facing experience: solutions engineering, technical onboarding, developer relations, or enterprise support.
- Experience with data/domain modeling: semantic layers, knowledge graphs, or object models over heterogeneous data sources.
- Experience integrating with enterprise or operational systems (internal platforms, legacy APIs, writeback to systems of record).
- Experience with evaluation frameworks and observability for LLM applications.
- Familiarity with sandboxing/isolation technologies.
- Contributions to open-source agent frameworks, MCP servers, or developer tools.
- Experience driving adoption of developer tooling within engineering organizations.
- A fun, supportive and engaging environment.
- Opportunity to make significant impact on transportation revolution by the means of advancing autonomous driving.
- Opportunity to work on cutting edge technologies with the top talent in the field.
- Competitive compensation package.
- Snacks, lunches and fun activities.