Build backend systems supporting Spellbook's core functionalities and AI workflows. Collaborate closely with Product and Design to optimize user experience.
About the role
- Senior MLOps & Data Systems Engineer developing scalable ML systems for Lime’s Vision team. Collaborating on data and model workflows in a remote role based in Canada.
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 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.
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).
Benefits
- Offers Equity
- Offers Bonus
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