Senior MLOps & Data Systems Engineer

Role

Senior MLOps & Data Systems Engineer (Vision team) at Lime. You will build and scale the core data and machine learning infrastructure that enables reliable, repeatable, and scalable model development, evaluation, and deployment.

Responsibilities

• Design & build scalable end-to-end ML pipelines spanning data ingestion, annotation, validation, training, evaluation, and deployment with reproducibility, consistency, and traceability.
• Develop and integrate annotation workflows with upstream ingestion and training systems to support task creation, labeling, QA, and dataset updates.
• Drive data-centric iteration by analyzing model performance/failures and connecting production signals, data mining, and annotation into tight feedback loops.
• Enable experimentation & reproducibility using experiment tracking, dataset versioning, and model lineage for reliable comparison across experiments.
• Build ML-tailored CI/CD workflows for automated testing, validation, and deployment of models and pipelines.
• Support model deployment to edge environments, collaborating with embedded and platform teams to ensure compatibility, performance, and reliability.
• Implement monitoring, logging, and feedback systems to track production performance and fuel continuous improvements.
• Optimize compute for training and inference across cloud environments, including efficient GPU/compute utilization.
• Collaborate cross-functionally with applied scientists, embedded engineers, and data teams to align data workflows, model development, and deployment.

Requirements

• Strong expertise in MLOps, data systems, and machine learning infrastructure.
• Experience building production-grade end-to-end ML pipelines.
• Ability to integrate annotation workflows and enable continuous iteration via data-model feedback loops.
• Strong CI/CD and experimentation/reproducibility practices for ML systems.

Nice-to-haves / Context (implied)

• Experience with edge model deployment and real-world monitoring/iteration.
• Familiarity with micro-mobility computer vision problems (e.g., tandem riding detection, precision parking validation, sidewalk riding prevention).

Location / Remote

• Remote, with a requirement to reside in the United States.