Advanced degree (Master's or Ph.D.) in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, Physics, or a related field.
Strong theoretical foundation in machine learning, deep learning, and computer vision, particularly with modern architectures.
Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow, alongside solid software engineering skills.
Self-motivated, strong analytical and problem-solving abilities, and a team player in a fast-paced environment.
Responsibilities
Participate in developing, training, and optimizing deep learning models for autonomous driving.
Engage in the entire machine learning workflow, including data curation and modeling experimentation.
Collaborate with cross-functional teams to integrate machine learning components into the production pipeline.
Stay informed on the latest research in computer vision and generative AI and bench-test state-of-the-art methods.
Benefits
Work in a cutting-edge field focused on autonomous driving and advanced robotics.
Collaborate with experienced domain experts and cross-functional teams.
Opportunity to contribute to impactful technologies and innovations in machine learning.
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.
