Robotics AI Infra & MLOps Engineer

ROZOR; Hamilton; On-site; CAD 90,000 - 130,000; Full time

Job summary

ROZOR is looking for an ML infrastructure engineer to design and maintain data, training, and production pipelines that support machine learning and autonomy models for ROZOR robots.

You will build pipelines for ingesting, labeling, storing, and processing datasets, create annotation systems, manage training pipelines, and implement MLOps with CI/CD, Docker, Kubernetes, and cloud infrastructure. Edge deployment on Jetson/ARM is a plus.

Qualifications

• Bachelor's or Master's in CS, AI, or a related field.
• 2+ years in ML infrastructure, MLOps, or data engineering.
• Proficient in Python and ML tooling; cloud platforms familiarity.

Responsibilities

• Design and maintain pipelines for ingesting, labeling, storing, and processing robot datasets.
• Create annotation systems for perception and autonomy work.
• Manage training pipelines, experiment tracking, and evaluation frameworks.
• Implement MLOps practices for continual training, testing, and deployment of ML models.
• Integrate cloud infrastructure for scalable and distributed computation.
• Build simulation systems for synthetic data and reinforcement learning.
• Collaborate with autonomy, perception, and embedded teams on data needs.
• Ensure reproducibility, versioning, and traceability of datasets and models.
• Optimize inference for edge deployment on Jetson and ARM-based systems.
• Monitor model performance and data drift across deployed robots.

Skills

Python

CI/CD

ML tooling

Data pipelines

Experiment tracking

Cloud platforms

Edge deployment

Model training

Debugging

Scalability

Education

Bachelor's or Master’s in CS/AI

Tools

Docker

Kubernetes

Git

Terraform

Job description

ROZOR is looking for an ML infrastructure engineer to design and maintain data, training, and production pipelines that support machine learning and autonomy models for ROZOR robots.

You will build pipelines for ingesting, labeling, storing, and processing datasets, create annotation systems, manage training pipelines, and implement MLOps with CI/CD, Docker, Kubernetes, and cloud infrastructure. Edge deployment on Jetson/ARM is a plus.