4 months ago
Remote, CanadaMid Level
Responsibilities
- Design, develop, and deploy end-to-end machine learning pipelines for training, validation, and inference.
- Implement MLOps practices including CI/CD for ML models, model versioning, monitoring, and retraining.
- Optimize models through feature engineering, hyperparameter tuning, and scalable inference techniques.
- Work with structured and unstructured data using Pandas, NumPy, SQL, and related tools.
- Build modular, reusable, and production-ready models using machine learning design patterns.
- Collaborate with data engineers to develop high-performance training and inference data pipelines.
- Deploy and manage models on AWS, GCP, and Azure using Docker and Kubernetes.
- Maintain model performance through continuous monitoring, bias detection, and explainability techniques.
Requirements
- Proficiency in Python and familiarity with Scikit-learn, LightGBM, and PyTorch.
- Strong understanding of supervised and unsupervised machine learning algorithms.
- Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker.
- Hands-on experience with Pandas, NumPy, SQL, and NoSQL databases.
- Knowledge of cloud-based ML deployment and infrastructure management.
- Ability to implement efficient real-time and batch inference pipelines.
- Strong analytical and problem-solving skills for translating business needs into scalable ML solutions.
- Ability to work in a fast-paced environment and continuously improve ML processes.
Benefits
- Top-of-market compensation for top performers.
- Comprehensive health, dental, and vision benefits.
- $1,500 annual learning and home-office stipend.
- $1,000 annual wellness stipend.
- Monthly lunch stipend and commuter benefits.
- Paid parental leave.
- 20 annual PTO days plus unlimited sick days.
- Quarterly team gatherings and in-office amenities.
Tech Stack
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