# Algorithm Engineer, Deep Learning & Vision (New Grad) at Bot Auto

> United States · Junior · ML / AI · open. Source: https://alderhq.app/jobs/greenhouse%3Abotauto%3A5371947008 (read 2026-11-15).

## Facts (observed)

- Company: [Bot Auto](https://alderhq.app/companies/bot-auto)
- Location text: Houston, TX; Houston Office
- Where it can be done from (inferred): United States (evidence: "Location: Houston, TX; Houston Office")
- Posted: 2026-07-27 (111 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-12
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/botauto/jobs/5371947008
- Skills listed (inferred): Python, C++, PyTorch, LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (United States, n 22): p25 $104k, median $143k, p75 $162k

## Description (the employer's text)

### 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.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Abotauto%3A5371947008 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.