Top Machine Learning Jobs in Toronto

Design and operate a scalable ML platform for model training, deployment, and governance while optimizing performance and supporting data scientists in productionizing workflows.

Own Faire's ML platform architecture and standards across training, inference, feature management, governance, and MLOps. Architect scalable Databricks/Spark/Delta Lake/MLflow workflows, optimize cost and reliability, integrate LLM workflows, mentor engineers, and lead cross-functional initiatives to increase data science velocity.

Design and implement machine learning algorithms for robotic manipulation, focusing on RL and IL. Collaborate across disciplines to ensure integration and successful deployment.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

As an intern, you'll develop software and machine learning systems for robots, focusing on perception, intelligence, behavior, and real-time software integration.

The ML Engineer will develop and advance multimodal models for robot perception and behavior, integrating these systems into hardware while collaborating closely with robotics engineers.

Work on designing, training, and deploying scalable ML models for credit risk, fraud detection, and personalization. Build ML pipelines for low-latency inference, automate experimentation and hyperparameter tuning, support full ML lifecycle (feature engineering, evaluation, A/B testing, monitoring for drift), and experiment with deep learning and reinforcement learning techniques while ensuring compliance and scalability in a fast-growing FinTech.

Lead R&D on LLM training and inference optimization, train and evaluate large-scale models on Tenstorrent hardware, improve performance via techniques like speculative decoding, quantization, kernel fusion, flash attention and distributed training, diagnose system bottlenecks, and translate research into scalable production solutions through cross-functional collaboration.

Design, build, and deploy scalable, secure machine learning solutions—with emphasis on recommender systems. Produce design docs and representative models, establish ML best practices and quality control, lead engineering efforts, and collaborate cross-functionally to integrate ML products using cloud, MLOps, and data engineering tools.

Lead and scale a product-focused data science organization to build production ML systems for personalization, recommendation, experimentation, and causal inference. Define strategy, partner with Product and Engineering, own end-to-end ML lifecycle, mentor the team, and measure product impact through robust analytics and monitoring.

Lead cross-functional teams to define, build, and deliver production-grade AI/ML solutions. Translate business objectives into technically feasible architectures, drive roadmaps, enforce MLOps and engineering standards, ensure governance and model risk controls, and measure business value from models. Mentor colleagues and coordinate cloud-native deployment, monitoring, and lifecycle management of AI systems.

Collaborate to scale self-driving systems, build training frameworks, profile model performance, and evaluate emerging technologies for training and inference.

Lead the Inference ML team in developing tools and APIs for large-scale ML applications, enhancing performance and usability, while collaborating across engineering teams.

Own and automate the end-to-end ML lifecycle: build, deploy, monitor, test, and retire models at scale. Design production-grade, highly available ML infrastructure and CI/CD pipelines, implement governance, and collaborate with engineering and security teams.

Lead testing strategy and execution for ML API features, building scalable test frameworks and automation to validate accuracy, fairness, and performance of inference systems. Drive pre-deployment and production validation across distributed cloud, multi-region, and hardware-backed inference environments. Mentor SDETs, champion best practices, debug complex distributed issues, and lead cross-functional quality initiatives to improve test coverage and engineering efficiency.

Provide AI consulting and enterprise software solutions including AI strategy, generative AI, machine learning, intelligent automation, cloud and IoT integration, custom software and mobile app development, and enterprise modernization to improve efficiency and accelerate digital transformation across industries.

As a Machine Learning Ops Engineer, you'll develop robust code, build and maintain MLOps infrastructure, QA systems, and mentor junior engineers to enhance AI model performance for mining clients.

As a Forward Deployed ML Engineer, you will apply machine learning to mining operations, develop neural network architectures, and communicate findings to non-technical stakeholders while working independently.

Develop and deploy AI solutions for enterprise clients, focusing on large language models and agentic systems integration while ensuring quality, scalability, and security.

The Applied Machine Learning Research Scientist will translate ML techniques into scalable systems, improve LLM performance, and optimize workflows while collaborating closely with researchers and engineers.

Validate and review Generative AI and deep learning models, perform R&D on LLM evaluation and explainability, develop validation methodologies, apply statistical tests and benchmark models, and communicate findings to technical and non-technical stakeholders.

Design, develop, evaluate, deploy, and monitor predictive and generative AI/ML solutions (including LLMs and agentic AI). Lead model assessment, vendor model evaluation, testing, validation, A/B experiments, and lifecycle management while ensuring Responsible AI, governance, performance, and clear communication with cross-functional stakeholders.

Design, train, evaluate, and deploy ML models (including LLMs) to production. Build automated pipelines, integrate models into scalable systems, run experiments, and collaborate with product partners to drive business impact.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

Lead development of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks to enhance molecular engineering and experimentations.