16 hours ago
San Jose, CA, USASenior / Staff+
H1B sponsor
Base Salary
$170k - $339k/yr
Responsibilities
- Develop ML-powered systems for driving behavior decisions, scene understanding, and trajectory generation for Level 4 autonomous driving.
- Frame real-world driving challenges as ML problems and apply imitation learning, reinforcement learning, learned planners, and end-to-end models.
- Design algorithms that improve the safety, generalization, and reliability of autonomous-driving planning.
- Integrate ML-driven approaches with rule-based and optimization-based planning systems.
- Develop and evaluate large-scale ML models using real-world driving data and simulation, then integrate them into production systems under compute, latency, and safety constraints.
- Build scenario-mining, training, evaluation, closed-loop simulation, and real-world-performance measurement infrastructure.
- Collaborate with ML Infrastructure, Perception, Prediction, Planning, Mapping, and Systems Engineering teams to investigate failures and deploy improvements.
Requirements
- Bachelor's, master's, or Ph.D. in Computer Science, Machine Learning, Robotics, Electrical Engineering, Mechanical Engineering, or a related technical field, or equivalent practical experience.
- 3+ years of industry experience for Senior or 5+ years for Staff in machine learning, autonomous driving, robotics, or related fields.
- Track record of developing and deploying production ML models, ideally for real-time, embedded, or autonomous-driving systems.
- Strong proficiency in Python and C++ and hands-on experience with modern deep learning frameworks such as PyTorch, JAX, or TensorFlow.
- Deep understanding of deep learning techniques including transformers, BEV and 3D representations, and trajectory prediction or generation models.
- Foundations in linear algebra, control theory, or motion planning, including path and trajectory generation, trajectory evaluation, cost-function design, and lane and road geometry.
- Experience applying ML to motion planning, robotics, or related decision-making problems and integrating learning-based methods with traditional planning.
- Ability to own ambiguous technical challenges end to end while balancing research innovation, production constraints, safety, reliability, and engineering trade-offs.
- Demonstrated technical impact through research publications, open-source contributions, or significant industry achievements.
- Preferred: experience with closed-loop simulation, large-scale evaluation, road-structure representations, end-to-end autonomous-driving models, vision-language or foundation models, and real-time onboard inference optimization.
- Preferred: experience with TensorRT, CUDA, quantization, mentoring junior engineers, supervising research interns, or leading small technical teams.
- Preferred: publications at conferences such as CVPR, ICCV, ECCV, NeurIPS, CoRL, ICRA, IROS, or IV, or equivalent industry impact.
Benefits
- Base salary is listed separately for Senior and Staff positions; compensation also includes bonus, equity, and benefits.
- Compensation varies by role, level, location, skills, experience, and education or training.
Categories
ML EngineeringRobotics
About DiDi
DiDi is a mobility technology platform offering app-based ride hailing, taxi hailing, shared mobility, food delivery, and intra-city freight to consumers and businesses across Asia Pacific, Latin America, and other markets. It connects riders with drivers and couriers and provides vehicle, energy, and insurance-related services, earning transaction commissions and service fees. DiDi launched an autonomous driving unit in 2016, which became the independent DiDi Autonomous Driving in 2019 to develop Level 4 technology for its network.