ML Software Engineer

About UsWe’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 DoBuild 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 consumersIntegrate and operate simulators for closed-loop evaluation; build tooling for metrics, visualization, and experiment managementDeliver production-grade serving and inference tooling for deterministic, low-latency operation on bench/mule and eventual vehicle deploymentsOwn systems from scratch: architecture → implementation → testing → documentation → iteration; raise the bar on code quality, reliability, and observabilityWhat We’re Looking ForEducation & Experience: MS in CS/ML/Robotics or BS + ≥2 years building ML/data/evaluation systemsSoftware engineering excellence: Strong Python fundamentals (data structures, testing, debugging, modular design) and a track record of shipping production-quality code/APIs and reliable automationPipelines → Training → Serving: Demonstrated experience building data/ML pipelines and evaluation tooling, and integrating training and inference using PyTorch, TensorFlow, or JAXDatasets at scale: Dataset packaging, sharding, manifest formats, and integrity checks for large multimodal datasetsPerformance & 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 teamNice to have: Prior work on perception, detection, or multimodal modelsWe may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.The anticipated base salary for this position is expected to be within the following range. Your actual base pay will be determined by your job-related skills, experience, and relevant education or training.