Machine Learning/Deep Learning Engineer(PhD, New Grad)

About this role

<p><strong>Company Introduction</strong></p> <p>At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.</p> <h3>Key Responsibilities</h3> <ul> <li><strong>Model Implementation & Iteration:</strong> 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.</li> <li><strong>Full Lifecycle Execution:</strong> 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.</li> <li><strong>Cross-Functional Collaboration:</strong> Partner with simulation, infrastructure, and downstream planning/control teams to deploy, evaluate, and integrate machine learning components into our production pipeline for autonomous trucks.</li> <li><strong>Literature Tracking:</strong> 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.</li> </ul> <h3>Qualifications</h3> <h3>Required:</h3> <ul> <li><strong>Education:</strong> 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.</li> <li><strong>Core Knowledge:</strong> Strong theoretical foundation in machine learning, deep learning, and computer vision, with a solid understanding of modern architectures (e.g., Transformers, CNNs, Graphs).</li> <li><strong>Technical Stack:</strong> 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).</li> <li><strong>Attributes:</strong> High self-motivation, strong analytical and problem-solving skills, a fast learner in a high-velocity startup environment, and a strong team-player mindset.</li> </ul> <h3>Preferred (Targeted Research & Background):</h3> <ul> <li><strong>Specific Research Directions</strong>: Academic thesis or deeply focused research experience in one or more of the following domains:</li> <ul> <li>3D Computer Vision / Bird’s-Eye-View (BEV) Perception</li> <li>Online Mapping, Vectorization, or Visual SLAM</li> <li>Prediction and Behavioral Modeling</li> </ul> <li><strong>Academic Achievements</strong>: 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.</li> <li><strong>Engineering Plus</strong>: Hands-on experience with model deployment, quantization, distillation, or inference acceleration tools (e.g., TensorRT, ONNX, CUDA, C++).</li> <li><strong>Industry Exposure</strong>: Prior internship experience within the autonomous driving industry or advanced robotics labs is highly desirable.</li> </ul>