Advanced degree (Master's or Ph.D.) in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, Physics, or a related field.
Solid theoretical foundation in machine learning, deep learning, and computer vision.
Proficient in Python and frameworks like PyTorch or TensorFlow, with strong software engineering skills.
Demonstrated self-motivation and analytical skills, adaptable to high-paced environments.
Team-oriented mindset with a track record of effective collaboration.
Responsibilities
Participate in the development and optimization of deep learning models specific to autonomous driving.
Engage in the full machine learning workflow, from data curation to performance verification.
Collaborate across teams to integrate machine learning into production systems for autonomous trucks.
Stay updated on research advancements in computer vision and generative AI, applying them to real-world scenarios.
Benefits
Exposure to cutting-edge projects in autonomous driving and deep learning research.
Opportunity to collaborate with experts in simulation and planning for practical applications.
Access to a dynamic startup environment that emphasizes innovation and learning.
Potential for professional growth through hands-on experience in a high-impact industry.
Full Job Description
Key Responsibilities
Model Implementation & Iteration: Participate in the development, training, and optimization of state-of-the-art deep learning models for autonomous driving, with a focus on end-to-end architectures, including object detection, tracking, online mapping, and end-to-end planning.
Full Lifecycle Execution: Engage in the entire machine learning workflow under the guidance of domain experts, spanning from data curation and data analysis to model experimentation, hyperparameter tuning, and rigorous performance metric verification.
Cross-Functional Collaboration: Partner with simulation, infrastructure, and downstream planning/control teams to deploy, evaluate, and integrate machine learning components into our production pipeline for autonomous trucks.
Literature Tracking: Stay abreast of the latest research breakthroughs in computer vision and generative AI, and actively bench-test promising SOTA methods to solve real-world corner cases.
Qualifications Required:
Education: An advanced degree (Master's or Ph.D., including upcoming graduates) in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.
Core Knowledge: Strong theoretical foundation in machine learning, deep learning, and computer vision, with a solid understanding of modern architectures (e.g., Transformers, CNNs, Graphs).
Technical Stack: Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow, along with strong software engineering fundamentals (data structures, algorithms, and clean coding practices).
Attributes: High self-motivation, strong analytical and problem-solving skills, a fast learner in a high-velocity startup environment, and a strong team-player mindset.
Preferred (Targeted Research & Background):
Specific Research Directions: Academic thesis or deeply focused research experience in one or more of the following domains:
3D Computer Vision / Bird's-Eye-View (BEV) Perception
Online Mapping, Vectorization, or Visual SLAM
Prediction and Behavioral Modeling
Academic Achievements: A proven track record of research publications in top-tier machine learning, computer vision, or robotics conferences/journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICRA, IROS) as a primary contributor.
Engineering Plus: Hands-on experience with model deployment, quantization, distillation, or inference acceleration tools (e.g., TensorRT, ONNX, CUDA, C++).
Industry Exposure: Prior internship experience within the autonomous driving industry or advanced robotics labs is highly desirable.
