Humble Robotics is building an autonomous, zero-emissions hauler that lowers the cost of freight using vision-based AI. They are seeking an ML engineer to design, train, and ship the vision-language-action foundation model, working across architecture decisions, large-scale training, and simulation evaluation.
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