Software Engineer, ML Ops

📋 Description

• Build and maintain robust data pipelines that ingest field data, including rosbags, sensor logs
• Transform raw field data into curated, versioned datasets for perception and machine learning teams.
• Own dataset management: storage, indexing, querying, versioning, dataset delivery.
• Develop and maintain training workflows; improve efficiency and cloud infrastructure costs.
• Build internal tooling to accelerate perception engineering workflows: fast data access
• Develop metrics, monitoring, and diagnostics for dataset health, model performance, and pipeline

🎯 Requirements

• Bachelor’s or Master’s in Computer Science, Robotics, Data Engineering, or related field.
• Strong Python and ROS2.
• Practical knowledge of Docker and other DevOps/containerisation tools.
• Familiarity with AWS (S3, EC2).
• Solid understanding of ML workflows, data pipelines, and dataset versioning.
• Experience designing or maintaining reliable data infrastructure and automated workflows.

🎁 Benefits

• Base salary: CA$123,828–CA$154,785 for the Toronto position.
• Equity: Opportunity to participate in company equity.
• High-impact technical work: Build infrastructure for autonomous systems.
• Cross-functional environment: Work with ML, perception, robotics, and engineering specialists.
• Growth: Develop expertise across MLOps, data engineering, cloud infrastucture, and autonomous
• Innovation-focused environment: Tackle challenging problems in a fast-moving company.