Autonomous Driving Kit Software Engineer

Summary

Isuzu North America is looking to hire an Engineer I or Engineer II for their Autonomous Driving Kit Software Engineer position based in Sunnyvale, California. The role involves supporting and leading the development of Autonomous Driving software, verifying the developed software, conducting vehicle tests, and optimizing software systems for safe and reliable driving behavior.

Responsibilities

• Develops software for Autonomous Driving software stack (Perception/Localization, Planning/Prediction or Control)
• Collaborates on tasks with partnership organizations (including both Isuzu group companies and external companies) by participating in discussion/negotiation and reviewing documents/source code
• Analyzes driving log data and prepares data pipeline for ML model training
• Evaluates Autonomous Driving system performance by executing simulation/emulation
• Develops advanced technology or research in Autonomous Driving algorithm
• Supports vehicle testing to verify and evaluate the Autonomous Driving system
• Performs miscellaneous job-related duties as assigned

Skills

• Master's degree in Computer Science, Electrical Engineering, Robotics, Data science or related fields. PhD preferred
• Minimum one year of working experience in data analysis, robotics, programming, or automotive systems
• Fundamentals of autonomous driving, robotics, signal processing, and data science
• Academic background in autonomous systems, ML (DL/RL/VLM/LLM), vehicle dynamics, or simulation
• Depending on the experience level, understanding of ADAS/AD architecture, module interfaces, and production software
• Depending on the experience level, familiarity with ISO 26262 and functional safety standards
• Depending on the experience level, knowledge of end-to-end autonomous driving systems
• Domain-specific knowledge (based on role): Perception/Localization: Probabilistic filtering, sensor fusion, SLAM, GNSS/IMU, HD maps, image and point cloud processing, DL(CNN and Transformer)
• Domain-specific knowledge (based on role): Planning/Prediction: Path/trajectory planning, motion prediction, optimization, MRM, DL(RNN and Transformer)
• Domain-specific knowledge (based on role): Control: Classical/MPC control, vehicle dynamics, actuator modeling, RL for control tuning
• Strong analytical, problem-solving, and critical thinking
• Effective communication and teamwork, both independently and collaboratively
• Proficiency in Python and C++
• Experience with ML frameworks (PyTorch, TensorFlow), simulation tools, and robotic middleware (ROS 2)
• Depending on the experience level, familiarity with Docker, Bazel, CAN communication, and profiling tools (Nsight, nvprof, perf)
• Hands-on deployment of autonomous driving algorithms or DL models on embedded systems
• Control-specific tools: MATLAB-Simulink/Stateflow
• Depending on the experience level, practical experience in real-time testing, tuning, and closed-loop validation
• Experience with data transmission through Controller Area Network (CAN)
• Hands-on experience with TensorRT, CUDA, cuDNN, or custom GPU kernel optimization
• Understanding of ADAS/AD system architecture including interface between modules and production software development
• Knowledge of ISO 26262 or functional safety standards
• Familiarity with profiling tools (Nsight Systems, nvprof, perf)
• Hands-on experience deploying Autonomous Driving algorithms or DL models, in real-time systems or automotive environments (on embedded or automotive-grade hardware)
• Basic understanding of End-to-end autonomous driving system (e.g. BEV feature based, Vision-Language-Action Model)
• Understanding of probabilistic filtering (e.g., Kalman Filter, Particle Filter) and nonlinear optimization
• Solid understanding of computer vision and point cloud processing
• Solid understanding of deep learning architectures, including CNNs and Transformers
• Knowledge of GNSS/IMU error models and sensor calibration
• Experience with multi-sensor fusion (camera, LiDAR, radar)
• Practical experience implementing or adapting Graph-SLAM systems (e.g., g2o, GTSAM, Ceres Solver)
• Experience using HD maps, lane-level localization, and map matching techniques
• Practical experience implementing path planner (e.g. Dijkstra, A* algorithm) or trajectory planner (e.g. Frenet frame)
• Practical experience developing ML model of motion prediction or time series data analysis
• Solid understanding of deep learning architectures, including RNNs and Transformers
• Experience using HD maps, and basic understanding of map data format
• Basic understanding of optimization solver (e.g. QP Solver)
• Solid understanding of feasibility of planned trajectory under vehicle dynamic limits
• Knowledge of Minimum Risk Maneuver (MRM) concept and algorithm
• Solid understanding of classical control theory including PID controller
• Hands-on experience of tuning control performance by changing control parameters in test vehicle
• Solid understanding of Model Predictive Control (MPC)
• Basic understanding of vehicle dynamics (e.g. bicycle model) and actuator modeling constrains and latency (steering, throttle, brake, powertrain)
• Practical experience with integrated control, localization, and sensor fusion systems closed-loop testing (both simulation and on-road) is a plus
• Experience in applying Reinforcement Learning (RL) to vehicle controller or controller parameter tuning is a plus

Qualifications

Must Haves

• Master's degree in Computer Science, Electrical Engineering, Robotics, Data science or related fields. PhD preferred
• Minimum one year of working experience in data analysis, robotics, programming, or automotive systems
• Fundamentals of autonomous driving, robotics, signal processing, and data science
• Academic background in autonomous systems, ML (DL/RL/VLM/LLM), vehicle dynamics, or simulation
• Depending on the experience level, understanding of ADAS/AD architecture, module interfaces, and production software
• Depending on the experience level, familiarity with ISO 26262 and functional safety standards
• Depending on the experience level, knowledge of end-to-end autonomous driving systems
• Domain-specific knowledge (based on role): Perception/Localization: Probabilistic filtering, sensor fusion, SLAM, GNSS/IMU, HD maps, image and point cloud processing, DL(CNN and Transformer)
• Domain-specific knowledge (based on role): Planning/Prediction: Path/trajectory planning, motion prediction, optimization, MRM, DL(RNN and Transformer)
• Domain-specific knowledge (based on role): Control: Classical/MPC control, vehicle dynamics, actuator modeling, RL for control tuning
• Strong analytical, problem-solving, and critical thinking
• Effective communication and teamwork, both independently and collaboratively
• Proficiency in Python and C++
• Experience with ML frameworks (PyTorch, TensorFlow), simulation tools, and robotic middleware (ROS 2)
• Depending on the experience level, familiarity with Docker, Bazel, CAN communication, and profiling tools (Nsight, nvprof, perf)
• Hands-on deployment of autonomous driving algorithms or DL models on embedded systems
• Control-specific tools: MATLAB-Simulink/Stateflow
• Depending on the experience level, practical experience in real-time testing, tuning, and closed-loop validation
• Experience with data transmission through Controller Area Network (CAN)
• Hands-on experience with TensorRT, CUDA, cuDNN, or custom GPU kernel optimization
• Understanding of ADAS/AD system architecture including interface between modules and production software development
• Knowledge of ISO 26262 or functional safety standards
• Familiarity with profiling tools (Nsight Systems, nvprof, perf)
• Hands-on experience deploying Autonomous Driving algorithms or DL models, in real-time systems or automotive environments (on embedded or automotive-grade hardware)
• Basic understanding of End-to-end autonomous driving system (e.g. BEV feature based, Vision-Language-Action Model)

Nice to Haves

• Understanding of probabilistic filtering (e.g., Kalman Filter, Particle Filter) and nonlinear optimization
• Solid understanding of computer vision and point cloud processing
• Solid understanding of deep learning architectures, including CNNs and Transformers
• Knowledge of GNSS/IMU error models and sensor calibration
• Experience with multi-sensor fusion (camera, LiDAR, radar)
• Practical experience implementing or adapting Graph-SLAM systems (e.g., g2o, GTSAM, Ceres Solver)
• Experience using HD maps, lane-level localization, and map matching techniques
• Practical experience implementing path planner (e.g. Dijkstra, A* algorithm) or trajectory planner (e.g. Frenet frame)
• Practical experience developing ML model of motion prediction or time series data analysis
• Solid understanding of deep learning architectures, including RNNs and Transformers
• Experience using HD maps, and basic understanding of map data format
• Basic understanding of optimization solver (e.g. QP Solver)
• Solid understanding of feasibility of planned trajectory under vehicle dynamic limits
• Knowledge of Minimum Risk Maneuver (MRM) concept and algorithm
• Solid understanding of classical control theory including PID controller
• Hands-on experience of tuning control performance by changing control parameters in test vehicle
• Solid understanding of Model Predictive Control (MPC)
• Basic understanding of vehicle dynamics (e.g. bicycle model) and actuator modeling constrains and latency (steering, throttle, brake, powertrain)
• Practical experience with integrated control, localization, and sensor fusion systems closed-loop testing (both simulation and on-road) is a plus
• Experience in applying Reinforcement Learning (RL) to vehicle controller or controller parameter tuning is a plus

Benefits

• Comprehensive Health Coverage Medical, dental, vision, and fertility benefits to support you through every stage of life
• Fertility & Family-Building Support*through WIN Fertility*: Includes Adoption & Surrogacy Benefits, WINMaternity, and PowerPause, offering up to $25,000 in lifetime benefits for fertility-related services such as IVF, IUI, and preconception support
• Generous Time Off Enjoy a healthy work-life balance with paid vacation, 15 paid holidays annually, sick leave, parental leave, and MTO (Miscellaneous Time Off) for volunteering or a compelling personal need
• Smart Retirement Planning Build long-term financial security with a 401(k) plan featuring a company match and an additional Annual Retirement Contribution (ARC)
• Peace of Mind Company-paid Basic Life and AD&D Insurance, as well as Travel Insurance, so you're covered wherever life takes you
• Wellness Program Access a variety of tools and resources designed to support your physical and mental well-being
• Tuition Reimbursement Pursue continued education with financial support for job-related coursework, degree programs, and professional growth
• Exclusive Employee Discounts Save on insurance, travel, entertainment, car purchases/rentals, retail purchases, and more