ADAS Algorithm Engineer – Plan & Control Evaluation 智驾规控算法评测算法工程师
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Before you apply
LocationMultiple Locations
Typefull-time
SeniorityMid
Experience3+ yrs
DepartmentEngineering
Company size10,000+ people
First seenOct 18, 2026 · 2w ago
Verified live5d ago
At a glanceSummarised by Seekless from the posting.
Master in vehicle engineering, computer science, robotics, electrical engineering automation or a related field.
3+ years of working experience in autonomous driving, ADAS, or robotics domain.
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.
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.
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.
ADAS/AD domain, especially planning algorithm experience preferred.
Hands-on experience with one AD stack module (BEV perception, planning with learning-based methods, end-to-end models) is a plus.
German is a plus.
Skills
C++
Python
ROS
AI
Machine Learning
Cursor
Claude Code
GitHub Copilot
AutoSAR
ADAS
AD
Responsibilities
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;
Requirements
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