Software Engineer, ML Ops

Job summary

Build and maintain data infrastructure for an autonomous ground-handling fleet, transforming rosbags, sensor logs, and telemetry into curated, versioned datasets for perception teams. The role also develops training workflows and tools for fast data access, reproducible experiments, and automated evaluation, while tracking dataset health, model performance, and pipeline reliability and optimizing cloud costs.

Qualifications

• Bachelor's or master's degree in Computer Science, Robotics, Data Engineering, or a related field.
• Strong Python proficiency and working knowledge of ROS2.
• Working knowledge of Docker and other DevOps tools.
• Familiarity with cloud storage and compute, including AWS S3 and EC2.
• Understanding of machine learning workflows and dataset versioning.
• At least 2 years of MLOps or data infrastructure experience, ideally in robotics or autonomous systems.
• Experience with Weights & Biases, rosbag data, and large-scale sensor datasets.
• Working knowledge of C/C++.
• Experience supporting perception or machine learning research teams.

Responsibilities

• Build and maintain pipelines to ingest fleet rosbags, sensor logs, and telemetry.
• Convert raw field data into curated, versioned datasets and manage dataset storage, indexing, querying, and delivery.
• Set up training workflows and optimize cloud costs.
• Build tools for fast data access, reproducible experiments, and automated evaluation.
• Generate metrics and diagnostics for dataset health, model performance, and pipeline reliability.

Skills

Degrees

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Industry

Relocation