Hybrid Senior Machine Learning Engineer

at Magnet Forensics

• Job title: Senior Machine Learning Engineer
• Job type: Full Time
• Experience level: Senior
• Salary: CA$125,000 - CA$175,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Python, PyTorch, Tensorflow
• Location requirements: Hybrid, Canada

About the role

• Senior Machine Learning Engineer designing and optimizing ML/AI systems for digital forensic tools. Collaborating with cross-functional teams to lead initiatives and drive innovation in digital investigations.

Responsibilities

• Design, implement, and evaluate state-of-the-art ML/AI models and systems;
• Lead experiments, define success metrics, build evaluations, and iterate to improve performance, efficiency, and reliability;
• Collect, build, and work with complex, real-world datasets, developing preprocessing, augmentation, and feature engineering techniques that enhance model training and fairness;
• Design and prototype agentic workflows where models reason, plan, call tools, and collaborate with other systems to accomplish complex tasks;
• Collaborate cross-functionally with our Brain team to ensure models are production-ready, observable, scalable, and meet real user needs;
• Stay at the forefront of ML/AI research, assessing new techniques, frameworks, and trends, and translating them into practical innovations for our products;
• Contribute to building reusable research infrastructure and tooling that accelerates experimentation and improves reproducibility;
• Ensure ethical, responsible, and secure AI practices are integrated into model design, training, and evaluation;
• Mentor other engineers on ML and AI best practices, experimental design, evaluation methodology, and technical decision-making.

Requirements

• 5+ years of professional experience in machine learning or applied AI, with a track record of delivering models into production or production-ready pipelines
• Strong Python programming skills, with experience in building maintainable, scalable ML systems
• Experience designing and running experiments, selecting appropriate metrics, and evaluating models
• Practical experience working with large language models in production or research prototypes, including prompt engineering, fine-tuning or adaptation, and/or retrieval-augmented generation
• Hands-on experience with deep learning frameworks (eg, PyTorch, TensorFlow) and deployment frameworks (eg, Triton, TorchServer)
• Experience working with large, complex, and/or unstructured datasets, with a strong understanding of trade-offs between model quality, cost, inference speed, and system complexity
• Ability to work cross-functionally with engineers, researchers, product managers, and designers
• Strong communication skills for both technical and non-technical audiences
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience in applied ML research and engineering.

Benefits

• Generous time off policies
• Competitive compensation
• Volunteer opportunities
• Reward and recognition programs
• Employee committees & resource groups
• Healthcare and retirement benefits