Algorithm Engineer, Deep Learning & Vision (New Grad)

Botauto; Houston (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Bot Auto is crafting autonomous driving tech in Houston, with a focus on end-to-end ML for perception, mapping, and planning.

We offer mentorship from senior engineers, fast-paced growth, and a collaborative startup culture. You will work across data, models, and production systems to push state-of-the-art autonomous truck technology forward.

Qualifications

• Education requirement as stated: Bachelor's, Master's, or Ph.D. in Computer Science, Robotics, Applied Math, Physics, or related field.
• You have trained neural networks through coursework, research, personal projects, or internships.
• Solid foundation in ML/DL and modern architectures (Transformers, CNNs, Graphs).
• Proficiency in Python and DL frameworks (PyTorch), plus strong software engineering basics.

Responsibilities

• Model implementation, training, and optimization for autonomous driving tasks.
• Engage in the full ML lifecycle from data curation to hyperparameter tuning and performance verification.
• Collaborate with simulation, infrastructure, and downstream teams to deploy ML components into production.
• Stay updated on CV and generative AI advances and benchmark promising methods.

Skills

Neural networks

Python

PyTorch

Deep learning

Transformers

Education

Bachelor's/Master's/PhD in CS/Robotics/Math

Tools

CUDA

ONNX

TensorRT

C++

Job description

Company Introduction

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.

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.