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.
Remote Machine Learning Engineer Jobs in Canada (45)
Search = Machine Learning Engineer
Remote in = Canada
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.
The engineer will collaborate with researchers to execute research operations using existing infrastructure, focusing on characterizing neural network quality, failure modes, and edge cases based on research data. Key tasks include managing concurrent experiments, reviewing results, suggesting improvements, and writing technical reports detailing qualitative and quantitative outcomes.
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.
Senior Machine Learning Engineer, Personalization
DraftKings Inc.
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Full Time
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4 Days Ago
DraftKings Inc.
Lead the design and deployment of scalable machine learning systems to drive real-time personalization, player engagement, and retention. Collaborate with cross-functional teams to build ML pipelines and mentor other engineers on best practices.
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Design, build, and deploy production-grade machine learning systems for personalized recommendations, search, and advertising. Own the full ML lifecycle from problem definition and feature engineering to deployment and monitoring at internet scale.
Lead the design and development of scalable, end-to-end machine learning systems and applications aligned with organizational goals. Oversee program inception, manage ML pipelines, and mentor team members while championing Responsible AI practices.
Senior Machine Learning Engineer
Just Eat Takeaway.com
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Full Time
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1 Week Ago
Just Eat Takeaway.com
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.
The role involves designing and deploying production-grade machine learning architectures and generative AI solutions on Google Cloud. You will also lead client discussions, scope projects, and translate vague business requirements into technical ML models.
Senior Machine Learning Engineer - Power Factors
Opportunities with AppDirect's Advisor Partners (Recruitment as a Service)
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Full Time
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1 Week Ago
Opportunities with AppDirect's Advisor Partners (Recruitment as a Service)
Lead the architecture and training of foundational time-series models for renewable energy assets, focusing on tokenization and scaling from PoC to production. Build and optimize distributed training infrastructure and collaborate with cross-functional teams to align model outputs with business value.
Senior Machine Learning Engineer, Search & Recommendations
Instacart
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Full Time
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1 Week Ago
Instacart
Architect and build a unified ranking backbone for search, recommendations, and ads using multi-task learning and value-aware optimization. Develop production-grade models to optimize long-term metrics like LTV and incremental GTV while mentoring other ML engineers.
Design and operate the core AI platform components for training, deploying, and serving machine learning models at scale. Optimize inference systems for throughput and latency while managing GPU-based workloads and observability practices.
Design and deploy foundational AI/ML models and scalable machine learning pipelines to support business intelligence. You will mentor junior engineers and collaborate cross-functionally to integrate AI solutions into production applications.
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.
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.
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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.
The role involves analyzing complex AEC datasets to uncover patterns and translating these insights into data-driven narratives to guide AI strategy. You will design scalable ML pipelines and collaborate cross-functionally to prototype next-generation AI capabilities for AEC products.
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.
Principal ML Engineer, Machine Learning Platform and Systems Architecture
Autodesk
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Full Time
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6 Weeks Ago
Autodesk
Lead the design and evolution of large-scale machine learning platforms, focusing on infrastructure, data systems, and model lifecycle tooling. Drive technical strategy and execution for distributed training, inference services, and production observability across cross-functional teams.
You will build, deploy, and iterate on machine learning models and agentic workflows to address customer needs in the insurance sector. You will also monitor production systems, diagnose issues, and collaborate with engineering teams to ensure high-impact, reliable solutions.
You will design and build scalable VLM-based pipelines to generate high-quality labels while collaborating with embedded and data science teams. The role involves optimizing edge-to-cloud data strategies and benchmarking vision-language models for production environments.
Lead the design and development of computer vision and biometric systems, including face recognition and quality assessment. Own the end-to-end machine learning pipeline from data ingestion and synthetic data generation to production deployment on AWS.
You will lead the development and productionization of fraud prediction models using tabular, graph, and behavioral data. You will also collaborate with cross-functional teams to monitor model health and implement foundational improvements to the team's modeling workflows.
You will develop and scale fraud prediction models using tabular and behavioral data to make real-time transaction decisions. You will also collaborate with cross-functional teams to productionize models, monitor their health, and ensure operational robustness.
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The Senior Machine Learning Engineer will own the end-to-end lifecycle of high-impact ML projects, from offline experimentation to production deployment. They will also collaborate with cross-functional teams to enhance fraud detection effectiveness and scale infrastructure.
The role involves leading the design, development, and deployment of production, multi-turn LLM-powered features, such as summarization tools and clinician-facing conversational agents. Responsibilities also include owning backend services in Python, defining technical direction for AI strategies, and establishing evaluation approaches for LLM outputs.
The role involves designing modular and scalable real-time data pipelines capable of handling massive datasets and implementing custom Machine Learning algorithms within a low-latency environment. Responsibilities also include working on microservice architectures to manage the training, inference, and monitoring of thousands of ML models concurrently.
The engineer will be responsible for building, shipping, and owning product features end-to-end, applying techniques from classical ML to LLM-based approaches like RAG and prompt engineering. They will also write well-designed, maintainable code and participate in an Agile software development life cycle.
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