Senior Control System Engineer (Autonomous Driving)
About this role
We are looking for the best
AD Division์ Senior Control Systems Engineer๋ Autonomous Driving System์ ํต์ฌ ์ ์ด๊ธฐ ๋ฐ Motion Planning ์๊ณ ๋ฆฌ์ฆ์ ์ค๊ณ, ๊ตฌํ ๋ฐ ์ต์ ํํฉ๋๋ค. ์ ์ด ์ด๋ก ์ ๋ํ ์ํ์ ๊ธฐ๋ฐ ์ง์๊ณผ ์ฐจ๋ ๋์ญํ์ ๋ํ ๊น์ ์ดํด๋ฅผ ๋ฐํ์ผ๋ก, ์ค์ ์ฐจ๋ ํ๊ฒฝ์์ ์์ ์ ์ด๊ณ ์ ๋ขฐ์ฑ ์๋ ์ ์ด ์ฑ๋ฅ์ ๊ตฌํํ๋ ์ญํ ์ ์ํํฉ๋๋ค. ๋ํ Planning, AI, Systems Engineering ํ๊ณผ ๊ธด๋ฐํ ํ์ ํ์ฌ Learning-based Policy์ ์ ํต์ ์ธ ์ ์ด ์๊ณ ๋ฆฌ์ฆ์ ํตํฉํ๊ณ , ์ฐจ์ธ๋ Physical AI ๊ธฐ๋ฐ Autonomous Driving System ๊ฐ๋ฐ์ ๊ธฐ์ฌํฉ๋๋ค.
The Senior Control Systems Engineer in the AD Division is responsible for designing, implementing, and optimizing core controllers and motion planning algorithms for autonomous driving systems. This role requires a strong mathematical foundation in control theory and a deep understanding of vehicle dynamics to deliver reliable and robust control performance in real-world environments. You will work closely with planning, AI, and systems engineering teams to integrate learning-based policies with traditional control systems and contribute to the development of next-generation physical AI-powered autonomous driving systems.
Responsiblities
MPC, LQR/LQG, SMC, PID ๋ฑ ๊ณ ๊ธ ์ ์ด ์๊ณ ๋ฆฌ์ฆ ์ค๊ณ, ์๋ฎฌ๋ ์ด์ ๋ฐ ๋ฐฐํฌ
๋น์ ํ ๋ฌผ๋ฆฌ์ ์ ์ฝ ์กฐ๊ฑด ํ๊ฒฝ์์ ์ค์๊ฐ Trajectory Optimization ๋ฌธ์ ์ ์ ๋ฐ ํด๊ฒฐ
ROS2 ๊ธฐ๋ฐ ์ค์๊ฐ ๋ฐ ์์ ์ค์ฌ C++ ์ํํธ์จ์ด ๊ฐ๋ฐ
Planning ๊ฒฐ๊ณผ๋ฌผ, RL/IL ๊ธฐ๋ฐ Policy, Filtering ์๊ณ ๋ฆฌ์ฆ์ ์ ์ด ์์คํ ์ ํตํฉ
์ค์ฐจ ํ๊ฒฝ์์ ์ ์ด ์ฑ๋ฅ ๊ฒ์ฆ ๋ฐ ํ๋ผ๋ฏธํฐ ํ๋ ์ํ
์ฐจ๋ ์์ ์ฑ, ์น์ฐจ๊ฐ ๋ฐ ์์ ์ฑ ํฅ์์ ์ํ ์ ์ด ์์คํ ์ต์ ํ
Develop, simulate, and deploy advanced control algorithms including MPC, LQR/LQG, SMC, and PID
Formulate and solve real-time trajectory optimization problems under nonlinear physical constraints
Develop deterministic, real-time, and safety-critical software using C++ and ROS2
Integrate planning outputs, RL/IL-based policies, and filtering algorithms into the control architecture
Validate control performance and tune parameters on vehicle hardware platforms
Optimize control systems to maximize vehicle stability, ride comfort, and safety
Qualification
์ ์ด๊ณตํ, ์ ๊ธฐ๊ณตํ, ๊ธฐ๊ณ๊ณตํ, ์๋์ฐจ๊ณตํ, ๋ก๋ด๊ณตํ ๋๋ ๊ด๋ จ ๋ถ์ผ ์์ฌ ํ์ ์ด์๊ณผ 3๋ ์ด์์ ๊ฒฝ๋ ฅ ๋๋ ์ด์ ์คํ๋ ์ค๋ฌด ๊ฒฝํ
MPC, LQR/LQG, SMC, PID ๋ฑ ์ ์ด ์ด๋ก ์ ๋ํ ์ดํด ๋ฐ ์ ํยท๋น์ ํ ์์คํ ๋ชจ๋ธ๋ง ๊ฒฝํ
C++ ๊ธฐ๋ฐ ์๋ฒ ๋๋ ๋ฐ ์ค์๊ฐ ์ํํธ์จ์ด ๊ฐ๋ฐ ๊ฒฝํ
ROS2 ๋๋ ์ ์ฌ Middleware ํ๊ฒฝ์์์ ๊ฐ๋ฐ ๊ฒฝํ
Automotive ๋๋ Robotics ๋ถ์ผ์์ ์ค์ Hardware Actuator ์ ์ด ๋ฐ ์์ฐ ์์ค ๋ฐฐํฌ ๊ฒฝํ
Masterโs degree or higher in Control Engineering, Electrical Engineering, Mechanical Engineering, Automotive Engineering, Robotics, or a related field with 3+ years of professional experience, or equivalent industry experience
Strong understanding of control theory, including MPC, LQR/LQG, SMC, and PID, with experience in linear and nonlinear system modeling
Proficiency in embedded and real-time software development using C++
Experience developing within ROS2 or similar middleware environments
Proven experience controlling and tuning physical actuators in automotive or robotics systems through production deployment
Preferred Qualifications
์ ์ด๊ณตํ, ๋ก๋ด๊ณตํ ๋๋ ๊ด๋ จ ๋ถ์ผ ๋ฐ์ฌ ํ์
CasADi, OSQP, Ipopt ๋ฑ ์์น ์ต์ ํ Solver ํ์ฉ ๊ฒฝํ
์ค์๊ฐ QP/NLP ์ต์ ํ ๋ฌธ์ ์ค๊ณ ๋ฐ ๊ตฌํ ๊ฒฝํ
์์ฉ ์์ค Motion Planning ๋ฐ State Estimation Filter์ ๋ํ ๋์ ์ดํด๋
RL ๋๋ IL ๊ธฐ๋ฐ ์ ์ด ์ ์ฑ ๊ณผ์ ํ์ ๋๋ ํตํฉ ๊ฒฝํ
Generative AI, RL/IL ๊ธฐ๋ฐ Policy์ ์ ํต์ ์ ์ด ์์คํ ์ ๊ฒฐํฉํ ๊ฒฝํ
CDC, ACC, ICRA, IROS ๋ฑ ์ ์ดยท๋ก๋ณดํฑ์ค ๋ถ์ผ Top-tier ํํ ๋๋ ์ ๋ ๋ ผ๋ฌธ ๊ฒ์ฌ ๊ฒฝํ
ISO 26262, MISRA C++ ๋ฑ Automotive/Robotics ์์ ๋ฐ ์ฝ๋ฉ ํ์ค์ ๋ํ ์ดํด
Ph.D. in Control Theory, Robotics, or a related engineering discipline
Hands-on experience with numerical optimization solvers such as CasADi, OSQP, or Ipopt
Experience formulating and solving real-time QP/NLP optimization problems
Deep understanding of production-grade motion planning and state estimation algorithms
Experience integrating or collaborating with RL or IL-based control policies
Experience combining generative AI, RL/IL policies, and traditional control systems for safety-critical applications
Publication record in top-tier control and robotics conferences or journals such as CDC, ACC, ICRA, or IROS
Knowledge of automotive and robotics safety standards, including ISO 26262 and MISRA C++
Interview Process
์๋ฅ์ ํ - ์ฝ๋ฉํ ์คํธ - ํ์๋ฉด์ (1์๊ฐ ๋ด์ธ) - ๋๋ฉด ํน์ ํ์๋ฉด์ (3์๊ฐ ๋ด์ธ) - ์ต์ข ํฉ๊ฒฉ
์ ํ์ ์ฐจ๋ ์ง๋ฌด๋ณ๋ก ๋ค๋ฅด๊ฒ ์ด์๋ ์ ์์ผ๋ฉฐ, ์ผ์ ๋ฐ ์ํฉ์ ๋ฐ๋ผ ๋ณ๋๋ ์ ์์ต๋๋ค.
์ ํ์ผ์ ๋ฐ ๊ฒฐ๊ณผ๋ ์ง์์์ ๋ฑ๋กํ์ ์ด๋ฉ์ผ๋ก ๊ฐ๋ณ ์๋ด๋๋ฆฝ๋๋ค.
Resume Screening - Coding Test - Virtual Interview (approximately 1 hour) - Onsite or Virtual Interview (approximately 3 hours) - Final Offer
Please note that the interview process may vary depending on the position and is subject to change based on scheduling and other circumstances.
Interview schedules and results will be communicated individually via the email address provided in your application.
Additional Information
๋ชจ๋ ์ ์ถํ์ผ์ PDF ์์์ผ๋ก ์ ๋ก๋๋ฅผ ๋ถํ๋๋ฆฝ๋๋ค.
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