Top Machine Learning Jobs in Toronto

Four-month, full-time research fellowship focused on ML systems and performance. Fellows build and evaluate infrastructure-heavy projects (e.g., CPU simulators, accelerator backends, large-scale distributed training), use external models/APIs, receive mentorship, funding for compute, and aim to produce public research outputs.

Lead Vegetation Intelligence product, owning roadmap from ML detection models to customer-facing risk products. Advance detection models, integrate outputs into outage and ignition risk, run user research, and translate model capability into actionable B2B products used in the field.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

As an AI/ML DevOps Engineer, you will operationalize AI solutions, build CI/CD pipelines, and ensure production system reliability. Collaborate with data scientists and engineers to deliver AI capabilities and maintain strong operational practices.

Design and implement efficient algorithms to optimize LLM inference on DNN accelerators. Collaborate with mathematicians, ML researchers, and engineers to apply advanced algorithmic and numerical techniques for high-impact generative AI performance improvements.

The Machine Learning Engineer will create scalable ML systems, build production data pipelines, and ensure robust deployment and monitoring of solutions.

Design, build, and operate a cloud-native data and ML platform handling billions of events. Own end-to-end pipelines, feature stores, model training/serving, observability, and data product SLAs. Apply distributed systems, MLOps, and LLM/agentic systems to ensure reliable, low-latency access to features and production models with business-focused impact.

Define technical vision and roadmap for the company ML platform. Architect scalable ML pipelines, MLOps practices, security, observability, and integrations. Guide model training, deployment, serving, monitoring, and provide technical leadership and mentorship across engineering teams.

As a Research Intern, you will prototype and productionize research in AI and cryptographic schemes, collaborating with teams for real-world impact.

Build full-stack marketplace features and ML Ops infrastructure for search, ranking, and renter-facing products. Develop model deployment pipelines, feature stores, real-time data pipelines, and monitoring using Chalk and Vertex AI. Collaborate with data scientists to productionize models, improve system performance and reliability, write tested code, participate in reviews, plan technical work, and communicate effectively across distributed teams.

Build full-stack marketplace features for search, ranking, and renter-facing products while supporting ML Ops infrastructure. Develop model deployment pipelines, feature stores, real-time data pipelines, monitoring, and production integrations with data scientists. Write tested code, review contributions, plan technical work, and improve system performance, reliability, and scalability. Collaborate with product and globally distributed engineering teams.

Own the end-to-end machine learning lifecycle, including dataset creation, research, rapid experimentation, model development, deployment, monitoring, and optimization. Build production-grade ML and LLM solutions, apply techniques such as quantization and pruning, track experiments systematically, and maintain scalable MLOps pipelines. The role requires strong Python and PyTorch expertise, production ML experience, and hands-on knowledge of LLM fine-tuning, prompt engineering, RAG, and efficient inference.

The Machine Learning Engineer will design, develop, and deploy ML pipelines, optimize models, collaborate on data workflows, and implement MLOps best practices in a fintech environment.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

The role involves building scalable ML models, mentoring engineers, translating business requirements into specs, and ensuring solutions meet performance constraints.

Lead the design and development of software for quantum machine learning methods. Collaborate with researchers and communicate findings. Oversee best practices in machine learning systems and influence product offerings.

Design and build scalable .NET backend services and gRPC microservices; optimize data layers (SQL Server, MongoDB, Elasticsearch); implement cloud integrations on AWS and Azure; ship AI/ML features (LLMs, RAG, vector search, ranking, personalization) and operate MCP servers; contribute to CI/CD, observability, containerization, and production ML integrations impacting high-traffic consumer marketplaces.

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Design and build the organization's first ML platform: define architecture and roadmap, create self-service deployment frameworks, enable model registry, feature retrieval, inference routing, observability, and safe rollout workflows; mentor engineers and partner with Data Science to drive platform adoption and developer productivity.

As a Senior Machine Learning Engineer, you will design scalable AI systems, work with large datasets, write maintainable code, and continuously learn with a team of AI developers.

Lead and grow an ML infrastructure team to build, scale, and operate training, deployment, and agent/LLM systems across on-prem and cloud HPC. Partner with research, ML engineering, and platform teams to translate requirements into robust MLOps solutions, optimize GPU utilization, and establish company-wide ML infrastructure standards while mentoring engineers and driving model-driven practices.