Software Engineer, ML Ops

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Software Engineer, ML Ops based in Canada.

This role offers the opportunity to build the infrastructure that powers machine learning and autonomy systems in a real-world robotics <environment.You>’ll own critical data pipelines that transform fleet sensor data into reliable, versioned datasets for perception and ML teams.Your work will improve experimentation speed, model development, dataset quality, and operational <reliability.You>’ll also design training workflows and tooling while helping optimise cloud infrastructure and costs.The position combines software engineering, MLOps, data engineering, cloud infrastructure, and robotics <technologies.You>’ll work closely with technical teams to create reproducible workflows and accelerate the path from field data to production-ready models.This is a high-impact opportunity for an engineer who enjoys solving complex infrastructure challenges in a fast-moving autonomous systems environment.

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.