ML Engineer, Foundation Models

Team: Software Team

Location: San Francisco

Commitment: Full-time

Workplace Type: onsite

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.