Data and ML Infrastructure Engineer

Role overview Data and ML Infrastructure Engineer responsible for building the pipelines and tooling that turn large volumes of operational data into ML-ready datasets for an autonomy and robotics organization. The role centers on an internal video, imagery, and telemetry data lake serving autonomy, perception, and field operations teams.

Responsibilities - Build and maintain infrastructure for video, imagery, telemetry, sensor data, autonomy logs, mission data, and field-test data. - Own data ingestion, storage, indexing, metadata, access patterns, and lifecycle management within the internal data lake. - Develop scalable pipelines that transform raw operational data into curated datasets for ML training, evaluation, debugging, and analysis. - Build tools for searching, filtering, tagging, and retrieving data across platforms, missions, operating conditions, and events. - Build workflows to select, clean, label, validate, and version datasets, and support annotation pipelines for video, imagery, tracks, and telemetry. - Develop reproducible dataset-generation workflows for training, validation, regression testing, and benchmarking. - Integrate datasets with model training, experiment tracking, evaluation, and deployment workflows, including multimodal synchronization across sensors. - Implement automated checks for missing streams, corrupted files, synchronization issues, metadata gaps, labeling errors, and pipeline failures. - Establish standards for dataset quality, lineage, versioning, and reproducibility, and add monitoring around critical pipelines. - Build self-service tools that help engineers discover, access, analyze, and visualize operational data, and maintain clear internal documentation.

Requirements - Bachelor's degree in Computer Science, Data Science, Machine Learning, Electrical Engineering, Computer Engineering, Robotics, Applied Mathematics, or a related technical field. - Strong experience building data infrastructure, pipelines, or large-scale data systems. - Hands-on experience with multimodal data such as video, imagery, telemetry, or sensor streams. - Proficiency with cloud storage, distributed compute, and modern data tooling for ingestion, indexing, and querying at scale. - Experience with dataset versioning, lineage, and reproducibility practices. - Solid software engineering fundamentals, including code reviews, testing, and observability. - Strong cross-functional collaboration skills with autonomy, perception, software, and field teams.

Nice to have - Master's or PhD in a related technical field. - Experience with ML training and evaluation platforms such as PyTorch, TensorRT, ONNX, CUDA, MLflow, Docker, or Kubernetes. - Familiarity with simulation, synthetic data, and large-scale perception datasets. - Experience supporting defense, robotics, autonomy, aerospace, or dual-use technology programs. - Active or prior security clearance.

Benefits and work setup - 100% employer-paid health, dental, and vision insurance for employees and their families, plus employer-paid life insurance. - 401(k) with company match and an equity package. - Unlimited PTO with a two-week enforced minimum, and 16 weeks of paid parental leave. - Work-from-home stipend, monthly health and wellness stipend, and Global Entry reimbursement.