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

Botauto; Houston (TX); On-site; USD 120,000 - 170,000; Full time

Job summary

Bot Auto is building cutting‑edge autonomous trucks to transform freight transport. We seek a researcher/engineer to develop end‑to‑end ML models for perception, mapping, and planning, collaborating across teams in a fast‑paced startup environment.

You will stay at the forefront of CV and BEV research, publish results, and contribute to deployment with model optimization and inference acceleration.

Qualifications

• Advanced degree in a quantitative field as listed.
• Strong ML, DL, and CV foundations with modern architectures.
• Proficiency in Python and DL frameworks, plus software fundamentals.
• High self‑motivation, analytical skills, and teamwork in a startup.

Responsibilities

• Model implementation, training, and optimization for autonomous driving.
• End‑to‑end ML workflow from data curation to metric verification.
• Collaborate with simulation, infra, and planning teams to deploy components.
• Track literature and benchmark SOTA methods for real‑world edge cases.

Skills

Machine learning

Deep learning

Computer vision

Transformers

CNNs

Graphs

Education

Master’s or Ph.D. in Computer Science/Robotics/EE/Applied Mathematics/Physics

Tools

Python

PyTorch

TensorFlow

CUDA

ONNX

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 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.