Job description

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity. You will be a senior engineer on the team building our internal AI engineering platform — the systems, services, and automation that power how our engineers build and ship software. A core mission is connecting agents to real engineering decisions: giving agents a connected, semantic view of our systems and data, and letting them propose and safely execute actions against operational systems. The agentic layer (NL intake, agentic trouble-shooting, task orchestration, auto-research loop) is an important component, but the foundation of this role is strong software engineering: designing robust distributed systems, building reliable services and pipelines, and relentlessly automating manual workflows. You'll also work directly with internal and external customers to understand their workflows and drive adoption. Job Responsibilities: 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. Minimum Skill Requirements: 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. Preferred Skill Requirements: 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. What do we provide: 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. The base salary range for this full-time position is $174,720 - $295,680, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.

Requirements

Must-haves

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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningpythontypescriptapillm

Platform: greenhouse
Effort: quick