ADAS Algorithm Engineer – Plan & Control Evaluation 智驾规控算法评测算法工程师

• Evaluation Framework Development: Design, build, and maintain the foundational ADAS performance evaluation framework; e.g., data preprocessing modules, task scheduling engines, and report generation components.

• Performance Evaluator development: Develop scenario-specific Evaluator modules for L2, Active Safety, NGA sub-scenarios; Develop Rule-based Evaluator as baseline, iterate with data-driven parameter tuning using collected field data, build reusable data-driven tooling, and ultimately deliver a Module-based comprehensive Evaluator;

• Develop agents/skills that restructure Evaluation development workflows and collaboration modes, boosting both productivity and communication.Conduct Evaluation: using Golden Sample management and replay pipelines for both open-loop and closed-loop scenarios, covering data selection, version comparison, and regression validation.

• Reporting & Frontend Infrastructure: Develop task-triggering and result-visualization frontend systems for complex evaluation and analysis; support automated generation of major release gate reports and minor version evaluation reports, plus day-to-day operations;

• AI/Agent Toolchain: Leverage AI, Skills, and Agent technologies to transform development workflows and tooling (e.g., Scrum Master automation), enabling cross-functional teams to onboard quickly into our development ecosystem;

• Enablement for Non-Algorithm Teams: Based on AD stack code, develop Agent-powered tools for System Engineers and Validation teams (e.g., rapid debug tools driven by test data); provide SWE56 testing tools and bench support;

Qualification

Education:

§ Master in vehicle engineering, computer science, robotics, electrical engineering Automation or a related field.

Experience:

· 3+ years of working experience in autonomous driving, ADAS, or robotics domain; ADAS/AD domain, especially planning algorithm experience preferred; Understanding of L2/L2+ functional scenarios and common evaluation metrics;

· Familiarity with data processing and analysis workflows; Experience with data-driven development, parameter tuning, or applied machine learning; Hands-on experience with one AD stack module (BEV perception, planning with learning-based methods, end-to-end models) is a plus.

· 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

· Strong cross-team collaboration and communication skills; ability to work effectively with suppliers and multiple internal stakeholders.

· Familiar with C++ and Python programming; Familiar with ROS programming and related tools usage;

· Willing to share knowledge, self-motivated, and able to influence and lead the team with a positive, hardworking, and optimistic attitude.

Specific Knowledge

§ Proficient in automotive electronics industry systems, processes, standards, and toolchains; have an in-depth understanding and insights into the development trends of automotive products and technologies

§ Proficient knowledge on Plan & Control Algorithm

§ Proficient knowledge in AD SW development process

§ Good understanding in ADAS function Design

§ Good understanding on overall vehicle E/E architecture

§ Good understanding in CP and AP AutoSAR

§ Fluent English, German is a plus