Senior MLOps & Data Systems Engineer

Lime•,

4d•Remote

About The Position

Lime is seeking a high-impact Senior MLOps & Data Systems Engineer to join the Lime Vision team. This role focuses on designing and developing systems and workflows for reliable, repeatable, and scalable model development, evaluation, and deployment. You will tackle real-world micro-mobility problems by building pipelines that integrate data ingestion, annotation, training, evaluation, and deployment into a continuously improving system. The position emphasizes data-centric machine learning and end-to-end pipeline ownership, with a focus on strong data foundations and robust infrastructure driving model performance improvements. You will collaborate closely with applied scientists and cross-functional engineers to build and scale data and ML systems for model development, deployment, and continuous improvement in diverse real-world conditions. This is a remote position requiring candidates to reside in Canada.

Requirements

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).

Nice To Haves

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)

Responsibilities

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.
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.
Analyze model performance and failures, and drive targeted data improvements by connecting production signals, data mining, and annotation workflows into continuous feedback loops.
Implement systems for experiment tracking, dataset versioning, and model lineage to enable reliable comparison and iteration across experiments.
Develop and maintain CI/CD workflows tailored to ML systems, enabling automated testing, validation, and deployment of models and pipelines.
Collaborate with embedded and platform teams to support the deployment of models to edge environments, ensuring compatibility, performance, and reliability.
Implement monitoring, logging, and feedback systems to track model performance in production and drive continuous improvement through data and model iteration.
Optimize training and inference workflows across cloud environments, including efficient utilization of GPU and compute resources.
Work closely with applied scientists, embedded engineers, and data teams to ensure alignment across data workflows, model development, and deployment systems.
Participate in and improve the full ML lifecycle, from raw data ingestion and annotation through training, evaluation, deployment support, and post-deployment analysis.