Autonomy ML Engineer: Foundation Models & Pipelines

Humble Robotics; San Francisco (CA); On-site; USD 100,000 - 300,000; Full time

Job summary

Humble Robotics in San Francisco, CA builds the next generation of autonomous freight tech. This role focuses on creating scalable ML/data/evaluation systems, including multimodal pipelines and production-grade tooling for training, serving, and evaluation.

Qualifications include MS/BS with ML/data experience, strong Python, and experience with PyTorch, TensorFlow, or JAX. Join a fast-moving team focused on reliability and impact.

Qualifications

• MS in CS/ML/Robotics or BS with 2+ years building ML/data/evaluation systems
• Strong Python fundamentals with production-quality code experience
• Experience in building data/ML pipelines and evaluation tooling with PyTorch, TensorFlow, or JAX
• Experience with datasets at scale, packaging, sharding, manifest formats, and integrity checks
• Knowledge of ML/DevOps practices: cloud storage, CI/CD, observability
• Excellent communication and ability to work independently in a small team

Responsibilities

• Build the software backbone for autonomy-focused foundation models with multimodal data pipelines
• Implement and iterate on LLM, VLM, and VLA architectures; own model code paths
• Integrate simulators for closed-loop evaluation and tooling for metrics and experiment management
• Deliver production-grade serving and inference tooling for low-latency operation
• Own systems from architecture to testing and documentation with high standards

Skills

Python

ML systems

Data pipelines

Testing

Education

MS in CS/ML/Robotics

BS + 2+ years ML/data/evaluation

Tools

PyTorch

TensorFlow

JAX

Job description

Humble Robotics in San Francisco, CA builds the next generation of autonomous freight tech. This role focuses on creating scalable ML/data/evaluation systems, including multimodal pipelines and production-grade tooling for training, serving, and evaluation.

Qualifications include MS/BS with ML/data experience, strong Python, and experience with PyTorch, TensorFlow, or JAX. Join a fast-moving team focused on reliability and impact.