Top Machine Learning Jobs in Toronto

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.

Lead research and design of large-scale Biological Foundation Models (BioFMs) using genomic and single-cell data. Develop novel architectures and pretraining paradigms, implement and train models at scale, collaborate with biologists and drug developers, mentor junior researchers, and publish findings in top-tier venues.

Design, prototype, and productionize machine learning models for real-world problems using structured and unstructured data. Implement scalable ML systems, apply statistical methods, rapidly prototype research papers, and collaborate with engineering teams to deliver data-driven solutions.

Design, prototype, and productize scalable machine learning solutions using LLMs. Collaborate with clients and teammates to build production-ready systems, rapidly iterate prototypes, apply statistics and ML, and implement tooling and infrastructure for deployment and model-driven features.

Lead the Compass agent product as the technical and science owner: design and ship agentic/LLM-powered features end-to-end, set evaluation and experiment frameworks, build data-driven models and tooling, and collaborate across backend, data, and frontend to raise agent quality and enable safe action-taking behaviors.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

Lead the product vision for innovative AI technologies, translating research into marketable products, and collaborating with cross-functional teams.

Seeking a Research Scientist to develop AI models for enhancing 3D reconstruction processes, requiring strong skills in computer vision and neural reconstructions.

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Design and deploy closed-loop decision systems that estimate causal effects, quantify uncertainty, and optimize actions under constraints. Develop policy learning, bandits/decision-making, counterfactual evaluation, and production ML infrastructure to drive measurable business lift and safe automated decisions.

Lead design, development, and deployment of ML/AI solutions across the PureFacts platform. Own ML product vision and MLOps for multiple client models, build scalable data pipelines and cloud-based deployments, embed AI-driven automation and LLM features, ensure governance and explainability, and mentor a team of ML engineers to deliver measurable business impact.

Lead and mentor an ML engineering team to design, build, and ship LLM/GenAI and agentic systems (retrieval, tool use, orchestration, RAG). Collaborate with MLOps, product, and data science, review code, evaluate models, preprocess complex unstructured data, and deliver scalable, reliable AI features for a SaaS product in an agile environment.

Design, train, and fine-tune state-of-the-art language models and AI agents (with retrieval augmentation). Build efficient, scalable training and inference systems, stay current with ML research, and collaborate with product and design to deliver impactful applications.

Develop and innovate machine learning algorithms to maximize advertising ROI, prototype and test models on historical data, and implement production-ready code in collaboration with Data Engineers to scale solutions across a high-throughput programmatic advertising platform.

Define and execute the ML platform roadmap to accelerate data discovery, experimentation, deployment, inference, and AI integration. Partner with engineers and data scientists to remove workflow friction, build interoperable, scalable platform capabilities (inference, observability, foundation model support), analyze usage, and drive adoption through strong product experiences and cross-functional execution.

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.

Lead QA and validation for AI/ML systems in a GxP/GMP environment. Collaborate with data science, IT and business to design, validate, implement, and operate AI solutions under CSV/CSA. Author validation documentation, mitigate model and data risks, align stakeholders on compliant strategies, and drive continuous improvement and capability building in digital validation and model governance.

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

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

As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.