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.

• What You’ll Do
• 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.

About You

• 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 Py Torch or Tensor Flow.
• 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., Git Hub Actions, Git Lab 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).
• Preferred Experience
• Experience supporting computer vision or perception systems.
• Familiarity with annotation platforms (e. g., Labelbox) and large‑scale labeling workflows.
• Experience with experiment tracking tools (MLflow, Weights & Biases, or similar).
• Experience with workflow orchestration frameworks (Airflow, Argo, Prefect, or Kubeflow).
• Experience with dataset versioning and data‑centric ML approaches.
• Experience supporting edge or embedded ML deployment.
• Experience working with multi‑modal data (e. g., camera, IMU, GPS).

The base salary range listed reflects what Lime reasonably expects to offer for this role, with the final base salary determined by objective factors such as the candidate’s location and relevant skills and experience.

Depending on the position, the total compensation package may also include discretionary annual performance bonus opportunities and equity, subject to applicable plan terms and eligibility requirements.

Compensation

CA$141K – CA$194K. Offers Equity. Offers Bonus.

Lime is proud to be an Equal Opportunity Employer.

We believe different perspectives help us grow and achieve more.

That’s why we’re dedicated to building and developing a team that reflects a wider range of backgrounds, abilities, identities, and experiences.

If you require a reasonable accommodation during the application or hiring process, please email recruiting-operations@li. me for assistance.

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