Autonomous Driving Kit Software EngineerSunnyvale, CaliforniaOverviewSalary Range $111,059.00 - $157,168.00 Salary/year Level ExperiencedDescriptionWe are looking to hire an Engineer I or Engineer II. This position is based in Sunnyvale, California, and compensation is aligned with this designated work location.Supports/leads development of Autonomous Driving software such as Perception/Localization, Planning/Prediction or Control modules. Focuses on a single module to progressively expand into multiple modules as project demands and organizational priorities evolve. Verifies the developed software, conducts vehicle tests on test track or public road and runs simulation evaluations.Designs, implements, and optimizes software systems that enable safe, reliable, and intelligent driving behavior. Works on both rule-based and data driven approaches for autonomous driving software stack which may include onboard stack development, Machine Learning (ML) model, and data pipeline developments for ML training.
Works closely with Isuzu US and Japan teams, leading autonomous driving partner companies, and reputed research institutes on the development and validation of autonomous Depending on the experience level, position might work under close supervision from more senior staff and follow established procedures or independent judgment is required and can guide junior staffs.Why Join Us?Work with industry-leading partners and institutions on cutting-edge AI for future mobility and logistics solutionsGain hands-on experience in simulation, validation, and deployment of production driven AI systemsExposure to global collaboration across U.S. and Japan technical teamsOpportunity to make a meaningful contribution to the future of transportation and mobility autonomyPrincipal Duties & Responsibilities35% Develops software for Autonomous Driving software stack (Perception/Localization, Planning/Prediction or Control).30% Collaborates on tasks with partnership organizations (including both Isuzu group companies and external companies) by participating in discussion/negotiation and reviewing documents/source code.15% Analyzes driving log data and prepares data pipeline for ML model training.10% Evaluates Autonomous Driving system performance by executing simulation/emulation.5% Develops advanced technology or research in Autonomous Driving algorithm.5% Supports vehicle testing to verify and evaluate the Autonomous Driving system.7. Performs miscellaneous job-related duties as assigned.Organizational RelationshipsReports to: Supervisor, AutonomousEducation, Experience & TrainingMaster'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 systemsKnowledgeFundamentals of autonomous driving, robotics, signal processing, and data scienceAcademic background in autonomous systems, ML (DL/RL/VLM/LLM), vehicle dynamics, or simulationDepending on the experience level, understanding of ADAS/AD architecture, module interfaces, and production softwareDepending on the experience level, familiarity with ISO 26262 and functional safety standardsDepending on the experience level, knowledge of end-to-end autonomous driving systemsDomain-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)Planning/Prediction: Path/trajectory planning, motion prediction, optimization, MRM, DL(RNN and Transformer)Control: Classical/MPC control, vehicle dynamics, actuator modeling, RL for control tuningSkills and AbilitiesStrong analytical, problem-solving, and critical thinkingEffective communication and teamwork, both independently and collaborativelyProficiency 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 systemsControl-specific tools: MATLAB-Simulink/StateflowDepending on the experience level, practical experience in real-time testing, tuning, and closed-loop validationJob Specific SkillsExperience with data transmission through Controller Area Network (CAN)Hands-on experience with TensorRT, CUDA, cuDNN, or custom GPU kernel optimizationUnderstanding of ADAS/AD system architecture including interface between modules and production software developmentKnowledge of ISO 26262 or functional safety standardsFamiliarity 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)Preferred: Perception/Localization EngineerUnderstanding of probabilistic filtering (e.g., Kalman Filter, Particle Filter) and nonlinear optimization.Solid understanding of computer vision and point cloud processingSolid 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.Preferred: Planning/Prediction EngineerPractical 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 analysisSolid understanding of deep learning architectures, including RNNs and TransformersExperience using HD maps, and basic understanding of map data formatBasic understanding of optimization solver (e.g. QP Solver)Solid understanding of feasibility of planned trajectory under vehicle dynamic limitsKnowledge of Minimum Risk Maneuver (MRM) concept and algorithmPreferred: Control EngineerSolid understanding of classical control theory including PID controllerHands-on experience of tuning control performance by changing control parameters in test vehicleSolid 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 plusPhysical StandardsThe employee must be able to access, enter, and retrieve data using a computer. This is primarily a sedentary position in a controlled office environment which requires only occasional reaching, stooping, and lifting of office files, reports or records, typically weighing 5 lbs.
or less. Requires occasional light lifting (5-25 lbs). Must be able on rare occasions to bend, crawl, climb, crouch, kneel and reach above shoulder level in the performance of job duties. Must be able to work in hot and cold weather extremes.Position requires up to 5% travel, mostly domestic. Occasional overnight or weekend trips; rare chance of