Job Title: Machine Learning Scientist/ Engineer
Length of contract- 12 Months (Possible extension)
Start date: ASAP
Location: Toronto, ON
Hybrid- 2-3 days a week @ Toronto, ON 
Interview- 2 rounds (first panel, 45 min)

Machine Learning Scientist / Engineer
We are seeking a highly skilled Machine Learning Scientist/Engineer with deep expertise in designing, building, and deploying scalable ML solutions. The ideal candidate will have hands-on experience across the full machine learning lifecycle, including model development, optimization, and production deployment in cloud environments.
 
Responsibilities

• Design and implement machine learning models using Python, PyTorch, TensorFlow, and Keras, applying both parametric and non-parametric approaches.
• Develop and optimize deep learning architectures, including CNNs, for complex tasks.
• Build robust CI/CD pipelines for ML systems using tools like Jenkins, GitHub Actions, and ML flow to enable automated testing, deployment, and monitoring.
• Containerize ML applications with Docker and manage deployments using Kubernetes.
• Implement scalable data processing and streaming solutions leveraging Kafka and distributed systems.
• Collaborate with cross-functional teams to deploy ML solutions into production with high reliability and performance.
• Monitor, detect, and address model drift, pipeline drift, and long-term model degradation.

Qualifications

• Strong programming skills in Python and proficiency with major ML frameworks (PyTorch, TensorFlow, Keras). Proficiency in SQL for data querying, preprocessing, and analytical workflows.
• Hands-on experience with CI/CD, Dockerization, and Kubernetes for ML workflows.
• Deep understanding of modeling techniques, including parametric/non-parametric methods and CNN-based architectures.
• Knowledge of data lineage, pipeline drift, and model drift monitoring.
• Experience with distributed systems and streaming platforms such as Kafka.
• Proven ability to design end to end, scalable, production ready ML systems.
• Familiarity with data lineage, experiment tracking, and model versioning using tools such as MLflow or DVC.
• Strong understanding of statistical modeling and feature engineering techniques, including correlation analysis, PCA, dimensionality reduction, and basic feature selection methods.

  
 
At SPECTRAFORCE, we are committed to maintaining a workplace that ensures fair compensation and wage transparency in adherence with all applicable state and local laws. This position’s starting pay is: $ 60.00/hr.