ML Software Engineer

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.