Senior MLOps, Data Systems Engineer

This is a fully remote position, open to applicants in Canada.

📋 Description

• Development of ML Pipelines & Data Systems: Create, construct, and sustain scalable pipelines encompassing data ingestion, annotation, validation, training, evaluation, and deployment, ensuring reproducibility, consistency, and traceability throughout the entire ML lifecycle.

• Integration of Data & Annotation Pipelines: Design and merge annotation workflows with upstream data ingestion and training systems, facilitating efficient task creation, labeling, quality assurance, and dataset updates that directly contribute to model iterations.

• Data-Centric Iteration: Evaluate model performance and identify failures, driving targeted data enhancements by linking production signals, data mining, and annotation workflows into continuous feedback loops.

• Experimentation & Reproducibility: Establish systems for tracking experiments, versioning datasets, and maintaining model lineage to allow for reliable comparisons and iterations across various experiments.

• CI/CD for Machine Learning: Develop and uphold CI/CD workflows specifically tailored for ML systems, enabling automated testing, validation, and deployment of models and pipelines.

• Support for Model Deployment: Work alongside embedded and platform teams to assist in deploying models to edge environments, ensuring compatibility, performance, and reliability.

• Monitoring & Feedback Loops: Set up monitoring, logging, and feedback systems to oversee model performance in production and promote continuous improvement through data and model iterations.

• Compute Optimization: Enhance training and inference workflows across cloud environments, ensuring efficient utilization of GPU and compute resources.

• Cross-Functional Collaboration: Collaborate closely with applied scientists, embedded engineers, and data teams to guarantee alignment across data workflows, model development, and deployment systems.

• End-to-End Contribution: Engage in and enhance the complete ML lifecycle, from raw data ingestion and annotation to training, evaluation, deployment support, and post-deployment analysis.

⛳️ Requirements

• Over 5 years of professional experience in MLOps, ML infrastructure, data systems, Machine Learning Engineering, or similar roles.

• Proficient programming skills in Python, with experience in ML frameworks such as PyTorch or TensorFlow.

• Proven experience in building and maintaining comprehensive ML pipelines, covering data ingestion, annotation, training, evaluation, and deployment workflows.

• Background in designing or integrating annotation and data curation workflows, with an understanding of how labeled data influences model performance.

• Strong comprehension of dataset versioning, data lineage, and reproducibility within machine learning systems.

• Experience with experiment tracking and managing the model lifecycle.

• Familiarity with CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins) and their application to machine learning workflows.

• Knowledge of 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, such as noisy inputs, edge cases, and variability across environments.

• Excellent problem-solving and debugging abilities, especially 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

• Equity options available

• Performance bonus offered