文远知行Software Engineer-Onboard Infrastructure（2026届校招）

The Role This role is at a key connection point between the Autopilot related needs in China market and the development and production teams. Diagnostic related objectives: Empower aftersales organizations to be able to more quickly and effectively diagnose when the product is not functioning properly to facilitate appropriate repair or service actions. This is done through enhanced onboard or off-board diagnostic methodologies including runtime firmware, server data analysis, or troubleshooting procedures. Systems related objectives: Assist in the specification and design of Tesla's autopilot system with the goals of high reliability, efficiency, safety, while minimizing costs. This role pays special attention to the Asia Pacific market, and system improvements through an accurate understanding of Asia Pacific driving conditions, customer experience issues and product deficiencies. This is achieved in this role by engaging with standards and related third parties, and bringing visibility of market directions to development teams. Responsibilities: Debug systems and perform diagnostics on field failures both remotely - looking at data logged by the vehicle, and directly in the field. Coordinate with design and development teams to perform further root cause analysis with component owners where necessary. Work with quality and manufacturing teams to ensure new issues are fully investigated and that countermeasures are in place. Regular collaboration with worldwide service teams to ensure consistent and stable global service operations. Define requirements for on-board diagnostic methodologies for use in the aftersales world and work with the firmware development team to implement. Develop and document troubleshooting and remote diagnosis techniques with service technicians, technical support specialists and regional technical specialists. Be the subject matter expert liaison between service engineering team and system design, quality, and manufacturing teams by communicating the latest product changes and information to the rest of the team. Some occasional travel to perform onsite root cause analysis and training is necessary. Availability for occasional off-hours calls and work. Systems Responsibilities: Review existing and proposed standards and assess their impact on current or future Tesla products. Bring visibility to development teams by summarizing and distilling existing standards and market direction. Make proposals to engineering design and department heads on the direction Tesla should take. Engage with standards committees to represent and drive Tesla’s interests. Review user experience issues and deficiencies, and escalate those to development teams that can be addressed by firmware or hardware improvements and/or revised functional specifications. Collaborate tightly with Tesla’s hardware and software teams to ensure efficient and economical implementation and validation of new features, components, and systems. Identify improvements for serviceability of the product for the purpose of reducing costs.

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Assist in designing, coding, and testing software applications based on project requirements. Participate in code reviews and contribute to team knowledge sharing. Collaborate with cross-functional teams including QA, product, and design teams. Write clean, maintainable, and well-documented code. Troubleshoot, debug, and upgrade existing systems. Stay updated with new technologies and development practices.

• Design, build, and maintain scalable compute pipelines for A/B scorecard calculation, supporting both first-party and future third-party customers.  • Develop and optimize distributed systems for high-performance experimentation infrastructure.   • Implement and tune big data technologies (e.g., Hadoop, Spark) to ensure efficient processing of large-scale experiment data.   • Collaborate with engineers, product managers, and stakeholders to define requirements and deliver impactful experimentation solutions.   • Apply A/B testing methodology to drive data-driven decision-making across Microsoft.   • Monitor, troubleshoot, and improve the reliability and performance of experimentation pipelines.   • Contribute to a culture of innovation, continuous learning, and knowledge sharing within the team.