Lime
Senior MLOps & Data Systems Engineer
United States · Senior
Sponsorship not specifiedDetected 72 days ago
PythonGitAWSCloud PlatformsDockerCI/CDGitHub ActionsJenkinsMachine LearningPyTorchAirflowComputer VisionMLOpsAI OrchestrationA/B TestingSystems EngineeringElectrical EngineeringSensorsTest AutomationCollaborationProblem Solving
About the role
- 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.
- 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.
- 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.
Responsibilities
- 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 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.
- 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.
- 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.
- 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.
- Experience building and maintaining end-to-end ML pipelines, including data ingestion, annotation, training, evaluation, and deployment workflows.
- That's why we're dedicated to building and developing a team that reflects a wider range of backgrounds, abilities, identities, and experiences.
Requirements
- Strong programming skills in Python, with experience in ML frameworks such as PyTorch or TensorFlow.
- Experience designing or integrating annotation and data curation workflows, and understanding how labeled data impacts model performance.
- Experience with experiment tracking and model lifecycle management.
- 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.
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).
- 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.
Nice to have
- Preferred Experience:
Compensation
- 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.
Benefits
- Complimentary use of Lime vehicles in participating cities, a monthly phone allowance, dedicated learning and development days, and access to perks including One Medical, Wellhub, and Headspace.
- 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.
- 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.
Equal opportunity
- Equal Opportunity Employer.
- If you require a reasonable accommodation during the application or hiring process, please email recruiting-operations@li.me for assistance.
This listing is sourced directly from Lime's careers page and normalized into a canonical job model.