42 Machine Learning Engineer jobs in Canada available for remote work from home. Apply for positions such as Machine Learning Engineer, Machine Learning Engineer , Machine Learning Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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You will design, develop, and deploy foundational AI and ML models while building scalable pipelines to support advanced analytics. You will also collaborate with cross-functional teams to embed ML features into live applications and establish robust governance and observability standards.
Develop machine learning models to automate the generation of scientifically accurate, editable visuals from research inputs. Implement natural language-based editing tools to enable intuitive figure creation while maintaining scientific integrity.
The engineer will own the end-to-end Machine Learning lifecycle, including researching, designing, and implementing novel ML solutions for complex business problems. Key tasks involve rapid prototyping, rigorous experiment tracking, deploying models using CI/CD, and maintaining performance monitoring systems.
Design, develop, and deploy machine learning solutions for ads systems including ranking, bidding, and auction models. Collaborate with cross-functional teams to translate business needs into impactful research directions and operational improvements.
Design, develop, and deploy machine learning models to power real-time inventory understanding and shelf stocking dynamics. Collaborate cross-functionally to build and evolve the core infrastructure of the Digital Twin Platform.
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Design, build, and deploy production-grade machine learning models and systems at scale to power core platform experiences. Own the full ML lifecycle, including problem definition, feature engineering, training, evaluation, deployment, and monitoring.
You will design and implement scalable machine learning pipelines while serving as a technical lead to mentor junior team members. Additionally, you will build modular infrastructure and CI/CD pipelines to transition research models into production-grade insurance applications.
You will build, deploy, and iterate on machine learning models and agentic workflows to address customer needs in the insurance sector. Additionally, you will monitor production systems, diagnose issues, and collaborate with engineering teams to ensure reliable, high-impact solutions.
You will develop and iterate on underwriting prediction models using tabular and sequential data to assess repayment risk. Additionally, you will build feature pipelines, prototype modeling ideas, and productionize models while monitoring their health and performance.
The role involves deploying commercial-grade AI products and integrating breakthrough innovations into the core AI model suite. You will also be responsible for engineering scalable ML systems and elevating the platform's standards for monitoring and automated testing.
Lead the evolution of an open-source interactive learning recommendation system from rule-based to personalized ML models. Build, deploy, and operate real-time recommendation services while defining evaluation methodologies and the scientific roadmap.
Lead the development and productionization of fraud prediction models using tabular, graph, and behavioral data. Collaborate with cross-functional teams to build scalable feature pipelines and monitor model health to combat evolving fraud patterns.
Lead the design and scaling of production-ready computer vision systems for biometric face recognition and attributes. Own the end-to-end ML pipeline from data ingestion and curation to low-latency deployment on AWS.
Design and implement modular, scalable real-time data pipelines and custom ML algorithms in low-latency environments. Develop microservice architectures to manage training, inference, and monitoring for thousands of concurrent ML models.
Design and build AI systems to uncover insights from business interaction data, focusing on information retrieval and recommendation systems. Own the full ML lifecycle from ideation and feature engineering to production deployment and observability.
Design, build, and deploy backend generative AI solutions to power automated, intelligent workflows. Convert complex R&D problems into production-ready software while maintaining ownership of the machine learning architecture.
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Define and build the architectural foundation for a self-service machine learning platform to enable Data Scientists to deploy and monitor models independently. Establish engineering standards, reusable tooling strategies, and a phased roadmap for the organization's ML infrastructure.
Develop and ship LLM-powered systems and build the foundational tooling and standards for AI implementation across the company. Own high-difficulty applied ML features from prototype to production, including the creation of eval pipelines and reference implementations.
Design and develop end-to-end computer vision models and pipelines for biometrics use cases such as face detection and recognition. Optimize models for low-latency inference and mentor other ML engineers on technical best practices.
Develop cutting-edge deep learning and machine learning technology to personalize user experiences across various Pinterest product surfaces. Collaborate with cross-functional teams to experiment with and improve ML models for retrieval and ranking.
Lead the strategic direction of machine learning infrastructure and architect complex ML pipelines for real-time and batch inference. Mentor mid-level engineers and collaborate with Data Science teams to optimize and deploy scalable models in production.
Lead the architecture and deployment of an enterprise-scale AI platform focusing on Generative AI, agentic systems, and RAG capabilities. Establish standards for model governance, security, and reliability while mentoring senior engineering staff.
Lead the technical vision and roadmap for ML systems focused on forecasting, supply positioning, and fleet optimization. Act as the primary technical decision-maker while mentoring engineers and partnering with product leadership to drive business outcomes.
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Lead the end-to-end machine learning lifecycle to design and deploy AI-first SaaS products using Large Quantitative Models. This includes building robust data pipelines, developing novel ML algorithms, and collaborating with cross-functional teams to create real-world MVPs.
Develop and maintain AI systems to automate customer operations such as disputes, returns, and fraud handling. Build LLM-powered pipelines for evidence extraction and prototype new modeling approaches to drive production improvements.
Design, develop, and deploy end-to-end machine learning pipelines and MLOps best practices. Optimize models and collaborate with data engineers to build scalable, production-ready inference systems.
Design and deploy LLM-powered features such as lab summaries and conversational agents for healthcare practitioners. Build and maintain backend services and evaluation workflows to ensure AI reliability and clinical relevance.
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