Senior Machine Learning Engineer

Location: REMOTE / Toronto, Ontario
This job allows you to work remotely.

We're working with an established technology company that is expanding its Machine Learning team and looking for a Senior Machine Learning Engineer to help build and productionize sophisticated AI systems at scale.
This is a hands-on senior engineering role focused on taking advanced machine learning concepts from experimentation through to production. You'll work across model development, ML infrastructure, deployment, optimization, monitoring, and evaluation while helping evolve the systems and tooling that support machine learning products used in a large-scale production environment.
The ideal candidate combines strong machine learning fundamentals with practical software engineering experience and has successfully deployed deep learning models as part of commercial products.
What You'll Do
- Design, build, and deploy production-grade machine learning models and AI-powered product capabilities.
- Take promising research and experimental approaches and turn them into reliable, scalable production systems.
- Develop and optimize deep learning models for high-performance inference in production environments.
- Improve the infrastructure and tooling supporting model training, deployment, orchestration, monitoring, testing, and evaluation.
- Build systems for tracking model performance and ensuring models continue to perform reliably after deployment.
- Work closely with engineering, product, and other technical teams to integrate machine learning capabilities into customer-facing products.
- Apply experimentation and A/B testing to evaluate model performance and business impact.
- Contribute to the technical direction of the ML platform and help establish engineering best practices.
- Mentor other engineers and provide technical guidance on machine learning architecture, implementation, and production systems.
- Stay current with advances in machine learning and evaluate where emerging techniques can provide practical product value.
The Opportunity
This is a strong opportunity for an experienced Machine Learning Engineer who wants to remain deeply technical while having meaningful influence over how sophisticated ML systems are designed and deployed.
You'll be working on production AI systems where model quality is only part of the challenge—the surrounding engineering, scalability, reliability, experimentation, and ability to translate machine learning advances into useful products are equally important.

Must Have Skills:
What We're Looking For
- 5+ years of professional experience developing machine learning or AI systems, including significant experience with deep learning and production model pipelines.
- Master's degree or higher in Computer Science, Mathematics, Engineering, or a related technical discipline.
- Demonstrated experience taking machine learning research or experimental models and turning them into production-grade applications.
- Strong understanding of modern neural network architectures and experience in areas such as transformers, natural language processing, computer vision, or multimodal learning.
- Hands-on experience fine-tuning and optimizing models for scalable, high-performance inference.
- Advanced experience with PyTorch and/or TensorFlow.
- Ability to understand, implement, and adapt techniques from current machine learning research.
- Strong Python development skills and experience with common machine learning libraries and frameworks such as scikit-learn, XGBoost, or similar technologies.
- Experience designing and operating production ML systems, including workflow orchestration, model and artifact versioning, automated testing, online/offline evaluation, performance monitoring, and experimentation.
- Strong understanding of the end-to-end machine learning lifecycle, from initial exploration and model development through deployment, monitoring, and ongoing evaluation.
- Experience with statistical modeling, predictive analytics, recommendation systems, personalization, or propensity modeling.
- Experience working with large-scale data processing technologies such as Spark is an asset.
- Experience working with cloud infrastructure; Google Cloud Platform experience is particularly relevant.
- Strong technical leadership skills with the ability to mentor engineers and influence technical decisions.
- Ability to communicate complex machine learning concepts clearly to both technical and non-technical stakeholders.