Senior Staff AI Engineer

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.   We are looking for a hands-on Senior Staff AI Engineer to build and scale production-grade AI systems that drive measurable impact across internal teams. This role owns the end-to-end delivery of applied AI solutions—partnering with product, engineering, and cross-functional teams across the organization to identify high-value use cases, and translating them into scalable systems using LLMs, retrieval, and agentic workflows. You will also define architecture and reusable patterns to enable AI adoption broadly, while setting a high bar for reliability, evaluation, and performance.   Job Responsibilities:

• Lead the technical design and hands-on development of prioritized AI applications, services, and platforms leveraging state-of-the-art LLM app stacks, retrieval-augmented generation, evaluation frameworks, and scalable serving.
• Define long-term architecture and engineering standards for Applied AI systems to maximize reuse, reliability, and impact across multiple product areas.
• Partner with business sponsors to translate high-value opportunities into roadmaps and shipped products with clear success metrics and measurable outcomes.
• Build a holistic view of AI investments by collaborating with adjacent engineering groups implementing AI in their domains, aligning patterns, reusing components, and avoiding duplication.
• Drive continuous improvement in AI methodologies and best practices; evaluate emerging capabilities and land them as secure, production-grade systems.
• Collaborate with stakeholders to embed robust governance, privacy, security, safety, and reporting practices across the AI lifecycle.
• Champion AI literacy, enablement, and adoption through demos, guidance, and technical leadership across the organization.
• Establish rigorous evaluation, guardrails, and monitoring practices; instrument offline and online metrics to ensure quality, safety, and SLOs.

  Minimum Skill Requirements:

• BS/MS/PhD in Computer Science or a related field, or equivalent experience.
• 10+ years of software engineering experience, with a proven track record delivering complex, production-grade systems.
• Deep technical expertise in AI/ML, with hands-on experience building and deploying systems using language models, retrieval/grounding (RAG), embeddings/vector search, and evaluation frameworks.
• Hands-on experience building and scaling agentic AI systems in production environments.
• Strong experience designing and implementing evaluation systems, including LLM-as-Judge frameworks, metric design, synthetic data generation, and agent benchmarking pipelines.
• Proven ability to establish feedback loops from production systems into evaluation and model improvement workflows.
• Expertise in AI/LLM observability and tracing, including instrumentation of systems and analysis of trace data for performance, latency, and correctness (e.g., OpenTelemetry-based tools such as LangFuse or equivalent).
• Experience architecting and deploying enterprise-scale AI systems or subsystems, with the ability to define technical direction, architecture, and reusable platform components across teams.
• Deep expertise in at least one of the following areas:
  • Agent memory systems (context management, long-term persistence, retrieval optimization).
  • AI gateways / tool orchestration layers (e.g., MCP, service integration, authorization, tool discovery).
  • Agentic workflows and orchestration (multi-step planning, tool calling, error recovery, concurrency).
• Demonstrated ability to translate ambiguous problems into scalable AI systems with measurable impact.
• Experience driving engineering standards, improving system reliability, observability, and performance (latency, throughput, cost).
• Strong familiarity with security, privacy, compliance, safety, and auditability in enterprise AI systems.
• Excellent communication and cross-functional collaboration skills; able to influence and align across engineering, product, and other internal teams.
• Proven ability to learn and apply new technologies quickly through hands-on development.