ADAS Core Development Engineer

This role is responsible for the development and mass production deployment of perception and localization algorithms for low speed systems. The primary focus is to bridge the gap between algorithm research and high-performance embedded implementation. You will lead the migration, optimization, and deployment of algorithms on automotive-grade platforms (such as Nvidia/Qualcomm/TI) to ensure robust, real-time performance and high-quality delivery for vehicle programs.

Key Responsibility:

· Production-Grade Algorithm Development:

o Design and implement perception algorithms (based on Vision/Ultrasonic fusion) for low speed scenarios, including obstacle classification (static/dynamic), and ground marking recognition.

o Develop high-precision relative localization solutions using multi-sensor fusion (Wheel Odometry, IMU, Steering Angle) based on EKF/UKF or other filtering frameworks.

· Embedded Deployment & Optimization:

o Lead the migration of algorithms to embedded platform, e.g. Nvidia/Qualcomm/TI;.

o Perform deep-level code optimization (C++14/17) and memory management to minimize CPU/RAM footprint while maximizing throughput.

o Utilize hardware-specific toolchains (TensorRT, TIDL, SNPE) to implement model pruning, INT8 quantization, and custom operator optimization.

· Data-Driven Iteration & Quality Assurance:

o Establish a data-driven loop to identify and resolve "long-tail" perception issues and localization drift in complex environments (e.g., low light, narrow spaces, non-standard markings).

o Conduct rigorous unit testing, SIL (Software-in-the-Loop), and HIL (Hardware-in-the-Loop) validation to ensure algorithm robustness.

· Functional Safety & Compliance:

o Ensure all software development adheres to automotive standards such as MISRA C++, ASPICE, and ISO 26262 functional safety requirements.

· Cross-Functional Collaboration:

o Work closely with System, Hardware, and Validation teams to troubleshoot urgent on-vehicle issues, ensuring zero-blocker delivery for critical project nodes.

Education Qualification

Master’s or PhD in Computer Science, Robotics, Electrical Engineering, or a related field.

No. of Years of Experience

At least 3 years of experience in mass production programs for low-speed features

Professional Exposure

(Technical Skills)

Mastery of C++ (OOP, STL, Multi-threading) and Python; experience with embedded Linux or QNX. Solid foundation in Deep Learning (BEV, Transformer, CNN) and classical estimation theory (Kalman Filters).

Preferred previous experiences

o Experience in at least one full lifecycle of mass production for low speed features (Start of Production experience).

o Hands-on experience in optimizing low-level kernels (e.g., CUDA, NEON) for automotive SoCs.

o Experience with professional automotive tools like CANoe/CANalyzer and HIL simulation.

Functional Skills

System thinking, root cause analysis, statistical data analysis, clear technical writing and presentation skills.

Behavioral Skills

Self-driven, proactive problem solver, strong communication skills, ability to work cross-functionally in global teams.

Special Knowledge and Skills  Required

Knowledge of ISO 26262.

Any Others

Ability to manage multiple tasks and prioritize effectively in a fast-paced environment.