Note: The job is a remote job and is open to candidates in USA. Humble Robotics is building an autonomous, zero-emissions hauler powered by vision-based AI for global logistics. The ML Engineer will design, train, evaluate, and deploy a vision-language-action foundation model for the company’s autonomous driving stack, working across model architecture, large-scale training, simulation, and vehicle integration.
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
Skills
- 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
- 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
Benefits
- Equity compensation
- Remote work
Company Overview