> Markdown version of [/jobs/ext/4624786-ml-software-engineer](https://www.wearedevelopers.com/jobs/ext/4624786-ml-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# ML Software Engineer

- **Company:** Humble Robotics
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $100,000 - $300,000
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Cloud Storage, Software Quality, Continuous Integration, Data Files, Data Structures, Shard (Database Architecture), Software Debugging, Python (Programming Language), Modular Design, Tensorflow, Software Engineering, Data Logging, Pytorch, Large Language Models, Containerization, Machine Learning Operations, Mixed Precision, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_67b14c231741ca79034442f65adeb743

## About the Role

* 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.

## Description

* 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.