Software Engineer, ML Ops

Accountabilities:

• • Build and maintain robust data pipelines that ingest field data, including rosbags, sensor logs, and fleet telemetry.
  • Transform raw field data into curated, versioned datasets that can be reliably accessed and used by perception and machine learning teams.
  • Own dataset management processes, including storage, indexing, querying, versioning, and dataset delivery.
  • Develop and maintain training workflows while identifying opportunities to improve efficiency and optimise cloud infrastructure costs.
  • Build internal tooling that accelerates perception engineering workflows, including fast data access, reproducible experiments, and automated evaluation pipelines.
  • Develop metrics, monitoring, and diagnostics to assess dataset health, model performance, and overall pipeline reliability.
  • Collaborate closely with perception, ML, robotics, and engineering teams to understand infrastructure requirements and improve development workflows.
  • Apply sound software engineering and DevOps practices to create reliable, maintainable, and scalable MLOps infrastructure.
  • Contribute to the continuous improvement of data and model development processes in a robotics and autonomous systems environment.

  Requirements:

  • Bachelor’s or Master’s degree in Computer Science, Robotics, Data Engineering, or a related technical discipline.
  • Strong Python programming skills and working knowledge of ROS2.
  • Practical knowledge of Docker and other DevOps or containerisation tools.
  • Familiarity with cloud storage and compute services, particularly AWS technologies such as S3 and EC2.
  • Solid understanding of machine learning workflows, data pipelines, and dataset versioning.
  • Experience designing or maintaining reliable data infrastructure and automated workflows.
  • Strong problem-solving abilities and attention to reliability, reproducibility, and data quality.
  • Ability to collaborate effectively with ML, perception, robotics, and software engineering teams.
  • Master’s degree in Computer Science, Robotics, or a related field is preferred.
  • 2+ years of MLOps or data infrastructure experience, preferably within robotics, autonomous systems, or another data-intensive technical environment.
  • Experience with Weights & Biases, rosbag data, or large-scale sensor datasets is an asset.
  • Working knowledge of C/C++ is preferred.
  • Experience supporting perception or machine learning research teams is a plus.
  • Willingness to work onsite in Toronto.

  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 supporting autonomous systems and real-world robotics applications.
  • Cross-functional environment: Work closely with ML, perception, robotics, and engineering specialists.
  • Opportunity for growth: Develop expertise across MLOps, data engineering, cloud infrastructure, and autonomous systems.
  • Innovation-focused environment: Contribute to challenging technical problems within a rapidly developing technology company.