As a global leader in micromobility, Lime is on a mission to build a future where transportation is shared, affordable and carbon-free. A Time Magazine 100 Most Influential Company, Lime has powered more than one billion rides in close to 30 countries across five continents, spurring a new generation of clean alternatives to car ownership. Learn more at <a href="http://li.me">li.me</a>.We are looking for a high-impact Senior MLOps &amp; 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:<ul style=""><li>ML Pipeline &amp; 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.</li><li>Data &amp; Annotation Pipeline Integradownstreamtion: 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.</li><li>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.</li><li>Experimentation &amp; Reproducibility: Implement systems for experiment tracking, dataset versioning, and model lineage to enable reliable comparison and iteration across experiments.</li><li>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.</li><li>Model Deployment Support: Collaborate with embedded and platform teams to support the deployment of models to edge environments, ensuring compatibility, performance, and reliability.</li><li>Monitoring &amp; Feedback Loops: Implement monitoring, logging, and feedback systems to track model performance in production and drive continuous improvement through data and model iteration.</li><li>Compute Optimization: Optimize training and inference workflows across cloud environments, including efficient utilization of GPU and compute resources.</li><li>Cross-Functional Collaboration: Work closely with applied scientists, embedded engineers, and data teams to ensure alignment across data workflows, model development, and deployment systems.</li><li>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.</li></ul>About You:<ul style=""><li>5+ years of industry experience in MLOps, ML infrastructure, data systems, Machine Learning Engineering, or related roles.</li><li>Strong programming skills in Python, with experience in ML frameworks such as PyTorch or TensorFlow.</li><li>Experience building and maintaining end-to-end ML pipelines, including data ingestion, annotation, training, evaluation, and deployment workflows.</li><li>Experience designing or integrating annotation and data curation workflows, and understanding how labeled data impacts model performance.</li><li>Strong understanding of dataset versioning, data lineage, and reproducibility in machine learning systems.</li><li>Experience with experiment tracking and model lifecycle management.</li><li>Familiarity with CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins) and applying them to machine learning workflows.</li><li>Experience with containerization (Docker) and workflow orchestration systems.</li><li>Experience with cloud-based ML environments (e.g., AWS) and distributed training workflows.</li><li>Strong understanding of real-world data challenges, including noisy inputs, edge cases, and variability across environments.</li><li>Strong problem-solving and debugging skills, particularly in complex, multi-stage systems.</li><li>Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).</li></ul>Preferred Experience:<ul style=""><li>Experience supporting computer vision or perception systems.</li><li>Familiarity with annotation platforms (e.g., Labelbox) and large-scale labeling workflows.</li><li>Experience with experiment tracking tools (MLflow, Weights &amp; Biases, or similar).</li><li>Experience with workflow orchestration frameworks (Airflow, Argo, Prefect, or Kubeflow).</li><li>Experience with dataset versioning and data-centric ML approaches.</li><li>Experience supporting edge or embedded ML deployment.</li><li>Experience working with multi-modal data (e.g., camera, IMU, GPS)</li></ul>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.#LI-PP1#LI-RemoteIf you want to make an impact, Lime is the place for you. Not sure if you meet all the qualifications? If this role excites you we encourage you to apply. Explore all opportunities on our career page.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.

SMART MOBILITY FOR THE MODERN WORLD:
Lime is founded on a simple idea that all communities deserve access to smart, affordable mobility.
Through the equitable distribution of shared scooters, bikes and transit vehicles, we aim to reduce dependence on personal automobiles for short distance transportation and leave future generations with a cleaner, healthier planet.

Senior MLOps & Data Systems Engineer

As a global leader in micromobility, Lime is on a mission to build a future where transportation is shared, affordable and carbon-free. A Time Magazine 100 Most Influential Company, Lime has powered more than one billion rides in close to 30 countries across five continents, spurring a new generation of clean alternatives to car ownership. Learn more at <a href="http://li.me">li.me</a>.We are looking for a high-impact Senior MLOps &amp; 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:<ul><li>ML Pipeline &amp; 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.</li><li>Data &amp; Annotation Pipeline Integradownstreamtion: 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.</li><li>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.</li><li>Experimentation &amp; Reproducibility: Implement systems for experiment tracking, dataset versioning, and model lineage to enable reliable comparison and iteration across experiments.</li><li>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.</li><li>Model Deployment Support: Collaborate with embedded and platform teams to support the deployment of models to edge environments, ensuring compatibility, performance, and reliability.</li><li>Monitoring &amp; Feedback Loops: Implement monitoring, logging, and feedback systems to track model performance in production and drive continuous improvement through data and model iteration.</li><li>Compute Optimization: Optimize training and inference workflows across cloud environments, including efficient utilization of GPU and compute resources.</li><li>Cross-Functional Collaboration: Work closely with applied scientists, embedded engineers, and data teams to ensure alignment across data workflows, model development, and deployment systems.</li><li>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.</li></ul>About You:<ul><li>5+ years of industry experience in MLOps, ML infrastructure, data systems, Machine Learning Engineering, or related roles.</li><li>Strong programming skills in Python, with experience in ML frameworks such as PyTorch or TensorFlow.</li><li>Experience building and maintaining end-to-end ML pipelines, including data ingestion, annotation, training, evaluation, and deployment workflows.</li><li>Experience designing or integrating annotation and data curation workflows, and understanding how labeled data impacts model performance.</li><li>Strong understanding of dataset versioning, data lineage, and reproducibility in machine learning systems.</li><li>Experience with experiment tracking and model lifecycle management.</li><li>Familiarity with CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins) and applying them to machine learning workflows.</li><li>Experience with containerization (Docker) and workflow orchestration systems.</li><li>Experience with cloud-based ML environments (e.g., AWS) and distributed training workflows.</li><li>Strong understanding of real-world data challenges, including noisy inputs, edge cases, and variability across environments.</li><li>Strong problem-solving and debugging skills, particularly in complex, multi-stage systems.</li><li>Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).</li></ul>Preferred Experience:<ul><li>Experience supporting computer vision or perception systems.</li><li>Familiarity with annotation platforms (e.g., Labelbox) and large-scale labeling workflows.</li><li>Experience with experiment tracking tools (MLflow, Weights &amp; Biases, or similar).</li><li>Experience with workflow orchestration frameworks (Airflow, Argo, Prefect, or Kubeflow).</li><li>Experience with dataset versioning and data-centric ML approaches.</li><li>Experience supporting edge or embedded ML deployment.</li><li>Experience working with multi-modal data (e.g., camera, IMU, GPS)</li></ul>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.#LI-PP1#LI-RemoteIf you want to make an impact, Lime is the place for you. Not sure if you meet all the qualifications? If this role excites you we encourage you to apply. Explore all opportunities on our career page.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.

Senior MLOps & Data Systems Engineer

About the Job

About the Company

Lime

Similar Jobs

Senior Machine Learning Infrastructure Engineer

Senior Machine Learning Infrastructure Engineer

Staff Machine Learning Engineer

Staff Machine Learning Engineer

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Senior MLOps & Data Systems Engineer

Senior MLOps & Data Systems Engineer

Senior MLOps Engineer

Senior MLOps Engineer

Senior Data Engineer

Senior Data Engineer

Staff Machine Learning Engineer, Supply

Staff Machine Learning Engineer, Supply

Engineering Manager, Lime Vision

Engineering Manager, Lime Vision

Senior Staff Machine Learning Engineer

Senior Staff Machine Learning Engineer

Senior Staff Data Engineer

Senior Staff Data Engineer