机器学习工程师，基础模型

About Humble Robotics

Working at Humble Robotics means taking on the biggest change in ground transportation in decades. We’re building an autonomous, zero-emissions hauler that dramatically lowers the cost of freight with groundbreaking vision-based AI, designed for today’s global logistics network.

We’re a fast-moving, close-knit team of AV industry veterans and innovative thinkers. We don’t believe culture can be engineered – but when it falls into place, it’s a once-in-a-lifetime adventure.

Progress has never felt so present.

Additional Information

As part of the interview process, we may use Artificial Intelligence (AI) tools to compare your qualifications and experience to the job description. A human reviews all AI output and makes a final hiring decision. Humble Robotics does not rely on the output to make any employment decisions. Some applicants may have a legal right to opt-out of the use of AI as part of our interview process. Contact legal@humblerobotics.ai to exercise this right or if you have further questions on the use of AI tools in our hiring process.

Humble Robotics is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, age, religion, disability, sexual orientation, veteran status, marital status or any other characteristics protected by law. Humble Robotics will consider qualified applicants with arrest and conviction records in a manner consistent with local ordinances.

Position Overview
We’re looking for an ML engineer to design, train, and ship the vision-language-action (VLA) foundation model at the core of Humble’s autonomous driving stack. You’ll work across the full arc—from architecture decisions and large-scale training to closed-loop evaluation in simulation and deployment on our trucks. This is a rare chance to build a production VLA for autonomous freight from the ground up, with the freedom and responsibility that comes with a small team tackling a massive problem.

Key Responsibilities

• Design and iterate on our VLA model architecture—including the VLM backbone, action decoder, and multimodal fusion pipeline

• Build and optimize large-scale training infrastructure (distributed training, data pipelines, mixed-precision, efficient fine-tuning)

• Develop simulation-based evaluation and closed-loop training workflows using photorealistic neural rendering

• Curate and manage multimodal training datasets spanning real-world driving and synthetic scenarios

• Translate state-of-the-art research (diffusion/flow-matching action heads, reasoning-augmented VLAs, world models) into production-grade systems

• Collaborate directly with vehicle systems and controls engineers to integrate model outputs into a real-time autonomous driving stack

Minimum Qualifications

• MS or PhD in Computer Science, Machine Learning, Robotics, or a related field—or equivalent industry experience

• Strong proficiency in PyTorch, distributed training, and GPU-accelerated workflows

• Solid foundation in transformer architectures, attention mechanisms, and modern generative modeling (diffusion, flow matching)

• Eligible to work in the United States

Preferred Qualifications

• Experience building or contributing to end-to-end autonomous driving systems

• Track record of publications at top ML/robotics venues (NeurIPS, ICLR, ICRA, CoRL) or significant open-source contributions

• Familiarity with sim-to-real transfer, photorealistic simulation, or neural rendering for driving scenes

• Experience with reinforcement learning, imitation learning, or learning from demonstration in embodied settings

• Comfort operating as an early team member—high ownership, low ego, fast iteration

Compensation
This role is eligible for base salary + benefits + equity compensation. Salary ranges are determined by role, level, and location. Within the range, individual pay is determined by additional factors, including qualifications, skills, experience, and location.