Senior MLOps & Data Systems Engineer

Lime is the largest global shared micromobility business, operating in close to 30 countries across five continents. We’re on a mission to build a future where transportation is shared, affordable and carbon-free. Our electric bikes and scooters have powered more than one billion rides in cities around the world. Named a 2025 Time 100 Most Influential Company, Lime continues to set the pace for shared micromobility globally, spurring a new generation of clean alternatives to car ownership.

We are looking for a high-impact Senior MLOps & Data Systems Engineer to help build and scale the core data and machine learning infrastructure for the Lime Vision team. In this role, you will focus on designing and developing the systems and workflows that enable reliable, repeatable, and scalable model development, evaluation, and deployment.

You will work on challenging, real-world problems in micro-mobility—such as tandem riding detection, precision parking validation, and sidewalk riding prevention—by building pipelines that connect data ingestion, annotation, training, evaluation, and deployment into a cohesive, continuously improving system. This role emphasizes data-centric machine learning and end-to-end pipeline ownership, with model performance improvements driven by strong data foundations and robust infrastructure.

This role requires strong expertise in MLOps, data systems, and machine learning infrastructure, with an emphasis on building production-grade pipelines, integrating annotation workflows, and enabling continuous iteration through tight feedback loops between data and models.

You will be part of the Vision team, working closely with applied scientists and cross-functional engineers to build and scale the data and ML systems that underpin model development, deployment, and continuous improvement in diverse and unpredictable real-world conditions.

This is a remote position with a requirement for candidates to reside in Canada to maintain effective collaboration across teams.

• 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.

• 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…