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 uphold scalable pipelines that encompass data ingestion, annotation, validation, training, evaluation, and deployment, ensuring reproducibility, consistency, and traceability throughout the entire ML lifecycle.

• Integration of Data & Annotation Pipelines: Develop and merge annotation workflows with upstream data ingestion and training systems, facilitating efficient creation of tasks, labeling, quality assurance, and dataset updates that directly enhance model iteration.

• Iteration Focused on Data: Examine model performance and failures, and promote focused 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 support reliable comparisons and iterations across experiments.

• CI/CD for Machine Learning: Create and manage CI/CD workflows specifically designed for ML systems, allowing for automated testing, validation, and deployment of models and pipelines.

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

• Monitoring & Feedback Mechanisms: Set up monitoring, logging, and feedback systems to oversee model performance in production and drive ongoing improvements through data and model iteration.

• Optimization of Compute Resources: Enhance training and inference workflows across cloud environments, focusing on effective utilization of GPU and compute resources.

• Collaboration Across Functions: Partner closely with applied scientists, embedded engineers, and data teams to ensure cohesion among data workflows, model development, and deployment systems.

• Comprehensive Contribution: Engage in and enhance the complete ML lifecycle, from the initial raw data ingestion and annotation through 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, along with experience in ML frameworks such as PyTorch or TensorFlow.

• Proven experience in constructing and maintaining end-to-end ML pipelines, covering data ingestion, annotation, training, evaluation, and deployment processes.

• Background in designing or integrating annotation and data curation workflows, with insight into how labeled data affects model performance.

• Solid understanding of dataset versioning, data lineage, and reproducibility in machine learning systems.

• Experience with tracking experiments and managing the model lifecycle.

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

• Proficiency in containerization (Docker) and workflow orchestration systems.

• Experience with cloud-based ML environments (e.g., AWS) and distributed training workflows.

• Strong grasp of real-world data challenges, including noisy inputs, edge cases, and variability across different 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 offerings

• Bonus offerings