ML Software Engineer

Role Overview

ML Software Engineer building the software backbone for autonomy-focused foundation models, supporting the full lifecycle from data pipelines to reproducible training/evaluation and production inference serving.

Responsibilities

• 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/VLA model architectures, owning model code paths including:
  • input/tokenization
  • inference runners
  • output heads for downstream consumers
• Integrate and operate simulators for closed-loop evaluation.
• Build tooling for metrics, visualization, and experiment management.
• Develop production-grade serving and deterministic, low-latency inference tooling for bench/mule use and eventual vehicle deployments.
• Own systems end-to-end: architecture → implementation → testing → documentation → iteration, improving code quality, reliability, and observability.

Requirements

• MS in CS/ML/Robotics (or BS + ≥2 years) building ML/data/evaluation systems.
• Strong Python fundamentals: data structures, testing, debugging, modular design.
• Proven ability to ship production-quality code/APIs and reliable automation.
• Experience building data/ML pipelines and evaluation tooling; integrating training and inference with PyTorch, TensorFlow, or JAX.
• Dataset at scale: packaging, sharding, manifest formats, and integrity checks for large multimodal datasets.
• Practical performance/optimization work for training and inference throughput/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).
• Strong communication and collaboration; ownership mindset in a small, fast-moving team.

Nice to Have

• Prior work on perception, detection, or multimodal models.