Senior Machine Learning Engineer – Canada

Who We Are; What We Do; Where We’re Going
Magnet Forensics is a global leader in the development of digital investigative software that acquires, analyzes, and shares evidence from computers, smartphones, tablets, and IoT-related devices. We are continually innovating so our customers can deploy advanced and effective tools to protect their companies, communities, and countries.
Serving thousands of customers globally, our solutions are playing a crucial role in modernizing digital investigations, helping investigators fight crime, protect assets, and guard national security.
With employees based around the world, Magnet Forensics has been expanding our global presence. As a part of Magnet Forensics, you can expect to make a difference in the world, no matter what role you play. You’ll be supported through learning and development, not to mention an incredible team with unbelievable talent and integrity.
If you think you would be the right person to join our team working towards this goal, we would love to hear from you!
Role Overview
We are looking for a Senior Machine Learning Engineer to join our team, designing, experimenting with, and optimizing applied ML and AI systems that power our digital forensics capabilities. You will lead the development of new models, training techniques, evaluation methods, and AI-powered systems that surface critical leads and insights for investigators, helping them solve cases faster and with greater confidence. As part of this team, you’ll work closely with Product, UX, and our Brain team to ensure our models and systems advance what’s possible while meeting real-world constraints. You’ll own complex initiatives end-to-end, including ideation and experimentation, to evaluation and handoff for integration, working with our team to advance the state-of-the-art in digital forensics.
What You’ll Do
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.
What We’re Looking For
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.
Nice to Have Skills
Experience with agentic systems, tool calling, multi-step reasoning workflows, or LLM evaluation frameworks;
Familiarity with vector databases, embedding models, and context retrieval strategies;
Background in NLP, computer vision, or other relevant ML domains;
Familiarity with MLOps tooling (eg, experiment tracking, model versioning, CI/CD for ML);
Contributions to open-source ML projects or publications in peer-reviewed venues;
Experience working with cloud providers like AWS or Azure;
Experience working with AI tools as part of your development workflow (eg, Claude, GitHub Copilot, etc.)
Compensation & Benefits
The Compensation range is for the primary location for which the job is posted. Please note that the actual compensation may vary depending on location and job-related factors such as qualifications, experience, knowledge and skills. If you are applying for this role outside of the primary location and you are selected for an interview, the Talent Acquisition Partner can share more information with you. If the compensation structure for the role includes an incentive component (i.e. most Sales roles) the range below represents total target compensation (TTC) (base salary + variable).
$125,000 – $175,000 (CDN) a year
Salary range (min – max)
Position Type: Current Vacancy
Magnet is proud to offer benefits such as:
– Generous time off policies
– Competitive compensation
– Volunteer opportunities
– Reward and recognition programs
– Employee committees & resource groups
– Healthcare and retirement benefits
Indicators of Success
We’re looking for someone who checks off most, but not all, of the boxes listed in “skills and experiences”. It’s more important to us to find candidates who can display indicators of success through skills they have developed and experiences they have been a part of, than to find folks who have ‘been there, done that”. We want to be part of your development journey, and we’ll learn as much from you as you learn from us.
How We Work
At Magnet Forensics, we take a hybrid-flexible approach to support your productivity and work-life balance. If you’re within a comfortable travel distance to one of our offices, you’ll occasionally join us in person. How often you’ll come in depends on your department and team needs, typically ranging from weekly to monthly. These in-person moments help us build stronger connections, spark new ideas, and celebrate our successes together. Most days, you can choose what works best for you, while staying in tune with your team’s goals.
We’re excited to welcome you to our team and look forward to achieving great things together – both in the office and wherever you work best!
The Most Important Thing
We’re looking for candidates that can provide examples of how they have demonstrated Magnet CODE in their previous experiences:
CARE – We care about each other and our mission to make a difference in the world.
OWN – We are accountable for our results – while never forgetting to act with integrity, empathy, and respect.
DEDICATE – We put our heart and soul into meeting the needs of our customers and helping them serve the people they protect.
EVOLVE – We are constantly innovating and exploring new ways to work together to make an impact with our work.
Here at Magnet Forensics, we are committed to continuous learning and are focused on building a diverse and inclusive workforce. This commitment will be reflected in our hiring processes and embedded in our values and how we treat one another. If you’re interested in this role, but do not meet all of the qualifications listed above, we encourage you to apply anyways.