ML Software Engineer

Job not on LinkedIn

🕒 August 4

🏄 California – Remote

🏄 California – Remote

💵 $100k - $300k / year

⏰ Full Time

🟢 Junior

🟡 Mid-level

🧑‍💻 Full-stack Engineer

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Humble Robotics

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2026

📦 Logistics

🚗 Transport

🚘 Automotive

Logistics • Transport • Automotive

Humble Robotics is a technology company building the Humble Hauler — a purpose-built, cabless autonomous electric freight vehicle that combines tractor, trailer, and driver functionality into a single platform. The company emphasizes vision-first AI autonomy, dock-to-dock capability (including direct unloading at docks), predictive safety and redundant failsafe systems, and a full-service operating model with 24/7 remote operational support and charging. The Humble Hauler is positioned to lower per-mile costs through a lighter, electric design that increases hauling capacity, eliminates diesel dependency, and targets reliable, compliant large-scale logistics operations.

📋 Description

• Build the software backbone for autonomy-focused foundation models, including multimodal data pipelines and reproducible training/evaluation workflows • Implement and iterate on LLM, VLM, and VLA architectures, including model code paths, tokenization, inference runners, and output heads • 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 architecture through implementation, testing, documentation, and iteration • Improve code quality, reliability, and observability

🎯 Requirements

• MS in CS/ML/Robotics or BS + ≥2 years building ML/data/evaluation systems • Strong Python fundamentals, including data structures, testing, debugging, and modular design • Track record of shipping production-quality code/APIs and reliable automation • Experience building data/ML pipelines and evaluation tooling • Experience integrating training and inference using PyTorch, TensorFlow, or JAX • Experience with dataset packaging, sharding, manifest formats, and integrity checks for large multimodal datasets • Practical experience improving training/inference throughput and latency, such as mixed precision, efficient batching, or model parallelism • Experience with cloud storage and training workflows, containerization, CI/CD, and experiment observability • Strong communication and collaboration with research and engineering partners • Bias for ownership and independence in a small, fast-moving team • Prior work on perception, detection, or multimodal models is nice to have