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梅赛德斯-奔驰租赁有限公司
Algorithm Closed Loop Replay Architect 智驾算法数据闭环架构师 ID212327
Skills
Data MiningPythonTesting & QAAIGitHubNo-code Automations
1 month ago
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Important information
Contract type:
Permanent
Location:
Beijing, China
Work mode:
Onsite
Published on:
20 September 2026
What they need
该职位来源于猎聘 Objectvie of Job
- Lead MB autonomous driving scenario dataset builds up, data production line operation and scaling, digital testing and performance evaluation with our scenario dataset: o Build, manage and operate Mercedes-Benz China scenario centric data production line, boost data production line efficiency and scaling scenario dataset for all AD/ADAS functions simulation and evaluation. o Coordinate MB China data-driven service provider activity to ensure automation of scenario data production and digital testing. Service providers include MB internal data collection team, data platform team, data mining team, GT production team, AD system simulation and automatic evaluation team. o Coordinate all data-driven service providers as 1 team to provide services for internal user requirements. o Coordinate AD customer function owner for MB China AD data driven function specification design, scaling and digital testing.
- Design and steer MB China AD data production line architecture, mange related tools requirement and development: o Define the overall cloud pipeline architecture for the AD scenario data closed-loop, covering scenario taxonomy, data flow, data mining, annotation, curation, quality assurance and digital evaluation layers o Manage requirements to tools and data pipeline, manage user experience optimization. o Own technical roadmap for scenario coverage expansion, data efficiency improvement, and toolchain evolution o Make build-vs-buy decisions for platform components; define integration patterns with internal and third-party systems
- Manage and develop data close loop for MB inhouse AD algorithm software o Build AD data close loop for MB inhouse AD algorithm software module development and evaluation o Enable inhouse algorithm development team digital testing and regression capability
- Manage data operation team to provide AD data operation service with high efficiency
Task Description
- Coordinate all service provider team as 1 team to deliver scenario dataset, enable AD system simulation and evaluation fully automatic
- Coordinate all service provider team to provide platform and data operation service for customer function owner
- Manage the tools requirement to AD toolchain team, tools quality acceptance, tools user experience improvement
- Coordinate all customer function owner team for data driven function specification implementation and scaling
- Enable data close loop, digital testing and system regression capability to internal algorithm team
- Manage external data operation team to deliver data and GT with high efficiency
Qualification
- BS/MS in Computer Science, Electrical Engineering, Robotics, or related field.
- More than 5 years working experience in autonomous driving, ADAS, or robotics domain.
- Project leader experience in AD algorithm development or large-scale AD data platform development end-to-end. Managed cross-functional programs spanning 3+ teams and 20+ engineers; comfortable with ambiguity and competing priorities.
- Daily active user of AI coding assistants (Cursor, Claude Code, GitHub Copilot, or equivalent); has built or orchestrated AI Agent workflows for real business tasks.
- Proficient in AD data, familiar with all kind of sensor data processing,
- Experience of building scenario libraries, scenario trees, or ODD (Operational Design Domain) frameworks for autonomous driving is a plus.
- Hands-on experience with at least one AD stack module (BEV perception, planning with learning-based methods, end-to-end models)
- Familiar with ROS programming and related tools usage, familiar with C++ and Python programming.
- Experience of built or deployed multi-agent AI systems for workflow automation beyond simple prompt chaining is a plus
- Experience of defined and operationalized data quality metrics, scenario coverage KPIs, or model-data feedback loops is a plus.
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