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.

• 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.