Algorithm Engineer, Deep Learning & Vision (New Grad)
Botauto · Houston, TX
About this role
## Algorithm Engineer, Deep Learning & Vision (New Grad) ### Company Introduction At Bot Auto, we’re revolutionizing the transportation of goods with 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, our team has achieved numerous world-firsts and unparalleled innovations. Join us and help transform dreams into reality. ### Key Responsibilities - **Model Implementation & Iteration:** Develop, train, and optimize state-of-the-art deep learning models for autonomous driving, focusing on end-to-end architectures (including perception, online mapping, and end-to-end planning). - **Full Lifecycle Execution:** Own the machine learning workflow—from data curation and analysis to 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 ML components into our production pipeline. - **Literature Tracking:** Stay current on breakthroughs in computer vision and generative AI, and bench-test promising SOTA methods for real-world corner cases. ### How You’ll Grow - **You get a real mentor:** You’ll be paired with senior-level engineers, with mentorship focused on design and judgment—how to frame problems, decide what to build (and why), and verify solutions are truly correct. - **We promote fast:** Managers are expected to push engineers to attempt work above their current level and promote in the next cycle when you deliver. ### Qualifications **Required** - **Education:** 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’ve run the loop—built a model, trained it, diagnosed why it failed, and fixed it. - **Core Knowledge:** Strong theoretical foundation in ML/deep learning, with understanding of modern architectures (e.g., Transformers, CNNs, Graphs). - **Technical Stack:** Proficiency in Python and deep learning frameworks such as PyTorch, plus strong software engineering fundamentals (data structures, algorithms, clean coding). - **Attributes:** High self-motivation, strong analytical/problem-solving skills, fast learning in a high-velocity startup environment, and a strong team-player mindset. **Preferred** - **Computer vision:** Research or projects in computer vision, particularly 3D. - **Specific research directions:** Academic thesis or deeply focused research in one or more of: - Computer Vision (2D or 3D) - Online Mapping, Vectorization, or Visual SLAM - Prediction and Behavioral Modeling - **Academic achievements:** Publications in ML, computer vision, or robotics conferences/journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICRA, IROS). - **Engineering plus:** Hands-on experience with deployment, quantization, distillation, or inference acceleration tools (e.g., TensorRT, ONNX, CUDA, C++). - **Industry exposure:** Prior internship experience in autonomous driving or advanced robotics labs.
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