Bachelor's, Master's, or Ph.D. in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, Physics, or a quantitative field.
Experience training neural networks through coursework, research, or internships; practical problem-solving in model training.
Strong theoretical foundation in machine learning and deep learning, including familiarity with modern architectures such as Transformers and CNNs.
Proficiency in Python and deep learning frameworks like PyTorch, alongside solid software engineering skills.
Attributes include high self-motivation, analytical skills, problem-solving capabilities, quick learning, and teamwork.
Responsibilities
Participate in development and optimization of advanced deep learning models for autonomous driving, focusing on perception and planning.
Engage in the complete machine learning workflow from data curation to performance metric verification under expert guidance.
Collaborate with cross-functional teams for the deployment and integration of machine learning components into production.
Stay updated on research in computer vision and generative AI, and evaluate promising methodologies for real-world applications.
Benefits
Real mentorship from senior engineers, focusing on problem framing, solution evaluation, and design judgment.
Opportunities for rapid advancement, with managers encouraging engineers to push their boundaries and promoting excellence.
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 perception, 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.
How You'll Grow
This matters as much to us as what you'll ship.
You get a real mentor. Every engineer is paired with senior-level engineers developing you. Mentorship here is weighted toward design and judgment: how to frame a problem, what to build and why, how to tell whether a solution is actually right.
We promote fast. Managers are expected to push engineers to attempt work above their current level, and to promote in the next cycle when they deliver it.
Qualifications Required:
Education: A Bachelor's, Master's, or Ph.D. (including upcoming graduates) in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.
You have trained neural networks. Coursework, research, personal projects, open-source work, and internships all count. We care that you have actually run the loop: built a model, trained it, found out why it was not working, and fixed it.
Core Knowledge: Strong theoretical foundation in machine learning and deep learning, with a solid understanding of modern architectures (e.g., Transformers, CNNs, Graphs).
Technical Stack: Proficiency in Python and deep learning frameworks such as PyTorch, 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:
Computer vision. Research or projects in computer vision, and particularly in 3D.
Specific Research Directions: Academic thesis or deeply focused research experience in one or more of the following domains:
Computer Vision (2D or 3D)
Online Mapping, Vectorization, or Visual SLAM
Prediction and Behavioral Modeling
Academic Achievements: A track record of research publications in machine learning, computer vision, or robotics conferences/journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICRA, IROS).
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.
