ML Software Engineer

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

About us

We’re building the next generation of ground transportation with advanced physical AI to simplify the toughest challenges in modern freight. Our stealth team, founded by the engineers who scaled autonomous driving, is developing an entirely new vehicle platform. We move fast, stay tightly aligned between engineering and product, and focus on creating reliable, real-world autonomous systems.

What You’ll Do

• Build the software backbone for autonomy-focused foundation models: design and ship multimodal data pipelines (ingest, validate, shard, package) and reproducible training/evaluation workflows (manifests, checkpoints, failure handling).
• Implement and iterate on LLM, VLM, and VLA architectures; own model code paths, input/tokenization, inference runners, and output heads for downstream consumers.
• Integrate and operate simulators for closed-loop evaluation; build tooling for metrics, visualization, and experiment management.
• Deliver production-grade serving and inference tooling for deterministic, low-latency operation on bench/mule and eventual vehicle deployments.
• Own systems from scratch: architecture → implementation → testing → documentation → iteration; raise the bar on code quality, reliability, and observability.

What We’re Looking For

• Education & Experience: MS in CS/ML/Robotics or BS + ≥2 years building ML/data/evaluation systems
• Software engineering excellence: Strong Python fundamentals (data structures, testing, debugging, modular design) and a track record of shipping production-quality code/APIs and reliable automation.
• Pipelines → Training → Serving: Demonstrated experience building data/ML pipelines and evaluation tooling, and integrating training and inference using PyTorch, TensorFlow, or JAX.
• Datasets at scale: Dataset packaging, sharding, manifest formats, and integrity checks for large multimodal datasets.
• Performance & optimization: Practical work improving training/inference throughput and latency (e.g., mixed precision, efficient batching, model parallelism).
• MLOps & infrastructure: Cloud storage and training workflows, containerization, CI/CD, and experiment observability (tracking, logging, metrics).
• Team fit: Strong communication, collaborative with research and engineering partners, and a bias for ownership/independence in a small, fast-moving team.
• Nice to have: Prior work on perception, detection, or multimodal models.

$100,000 - $300,000 a year

The anticipated base salary for this position is expected to be within the following range. Your actual base pay will be determined by your job-related skills, experience, and relevant education or training.