Senior MLOps, Data Systems Engineer

Lime

Senior MLOps & Data Systems Engineer developing scalable ML systems for Lime’s Vision team. Collaborating on data and model workflows in a remote role based in Canada.

Posted 5/11/2026full-timeRemote • 🇨🇦 CanadaSenior💰 CA$141,000 - CA$194,000 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudDockerJenkinsPythonPyTorchTensorflow

About the role

Key responsibilities & impact

ML Pipeline & Data Systems Development: Design, build, and maintain scalable pipelines that span data ingestion, annotation, validation, training, evaluation, and deployment, ensuring reproducibility, consistency, and traceability across the full ML lifecycle.
Data & Annotation Pipeline Integration: Build and integrate annotation workflows with upstream data ingestion and training systems, enabling efficient task creation, labeling, QA, and dataset updates that directly support model iteration.
Data-Centric Iteration: Analyze model performance and failures, and drive targeted data improvements by connecting production signals, data mining, and annotation workflows into continuous feedback loops.
Experimentation & Reproducibility: Implement systems for experiment tracking, dataset versioning, and model lineage to enable reliable comparison and iteration across experiments.
CI/CD for Machine Learning: Develop and maintain CI/CD workflows tailored to ML systems, enabling automated testing, validation, and deployment of models and pipelines.
Model Deployment Support: Collaborate with embedded and platform teams to support the deployment of models to edge environments, ensuring compatibility, performance, and reliability.
Monitoring & Feedback Loops: Implement monitoring, logging, and feedback systems to track model performance in production and drive continuous improvement through data and model iteration.
Compute Optimization: Optimize training and inference workflows across cloud environments, including efficient utilization of GPU and compute resources.
Cross-Functional Collaboration: Work closely with applied scientists, embedded engineers, and data teams to ensure alignment across data workflows, model development, and deployment systems.
End-to-End Contribution: Participate in and improve the full ML lifecycle, from raw data ingestion and annotation through training, evaluation, deployment support, and post-deployment analysis.

Requirements

What you’ll need

5+ years of industry experience in MLOps, ML infrastructure, data systems, Machine Learning Engineering, or related roles.
Strong programming skills in Python, with experience in ML frameworks such as PyTorch or TensorFlow.
Experience building and maintaining end-to-end ML pipelines, including data ingestion, annotation, training, evaluation, and deployment workflows.
Experience designing or integrating annotation and data curation workflows, and understanding how labeled data impacts model performance.
Strong understanding of dataset versioning, data lineage, and reproducibility in machine learning systems.
Experience with experiment tracking and model lifecycle management.
Familiarity with CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins) and applying them to machine learning workflows.
Experience with containerization (Docker) and workflow orchestration systems.
Experience with cloud-based ML environments (e.g., AWS) and distributed training workflows.
Strong understanding of real-world data challenges, including noisy inputs, edge cases, and variability across environments.
Strong problem-solving and debugging skills, particularly in complex, multi-stage systems.
Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).

Benefits

Comp & perks

Offers Equity
Offers Bonus

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonMLOpsML infrastructureMachine Learning EngineeringML frameworksPyTorchTensorFlowCI/CDdataset versioningdata lineage

Soft Skills

problem-solvingdebuggingcross-functional collaboration

Certifications

Bachelor’s degree in Computer ScienceMaster’s degree in Computer ScienceBachelor’s degree in Electrical EngineeringMaster’s degree in Electrical Engineering