ML Software Engineer
Humble Robotics
United StatesPosted about 1 month agoDiscoveredMatch locked
RemoteFull Time2+ years expDeveloper
About the role
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
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Education & Experience
MS in CS/ML/Robotics or BS + ≥2 years building ML/data/evaluation systems
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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.
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Pipelines → Training → Serving
Demonstrated experience building data/ML pipelines and evaluation tooling, and integrating training and inference using PyTorch, TensorFlow, or JAX.
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Datasets at scale
Dataset packaging, sharding, manifest formats, and integrity checks for large multimodal datasets.
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Performance & optimization
Practical work improving training/inference throughput and latency (e.g., mixed precision, efficient batching, model parallelism).
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MLOps & infrastructure
Cloud storage and training workflows, containerization, CI/CD, and experiment observability (tracking, logging, metrics).
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Team fit
Strong communication, collaborative with research and engineering partners, and a bias for ownership/independence in a small, fast-moving team.
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Nice to have
Prior work on perception, detection, or multimodal models.
Originally posted on Himalayas
Not included in the source posting: benefits.
Skills
machine-learningci-cdmlopspythonpytorchtensorflowartificial-intelligencellm
Who can apply
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