ML Software Engineer
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Job Description
ML Software Engineer building multimodal data pipelines, foundation models, and inference systems. Advancing reliable autonomous freight vehicles through physical AI and production-grade tooling.
Responsibilities:
- 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
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