Top Machine Learning Jobs in Toronto

The role involves leading AI/ML technical planning, evaluating new technologies, collaborating with stakeholders, and fostering innovation in the team.

The AI/ML Engineer will design, develop, and optimize machine learning models for mobile applications, collaborating with teams to enhance user experiences and innovate in AI-driven solutions.

As an AI/ML Engineer, you'll develop and optimize machine learning models for mobile applications, collaborate with teams, and integrate AI-driven features to enhance user experience.

The role involves developing infrastructure for benchmarking AI inference performance, analyzing system behavior, designing telemetry systems, and collaborating with teams to improve feature performance.

Manage and grow a small ML/GenAI engineering team while remaining hands-on: design and implement GenAI systems (agents, RAG, tool use), build NLP solutions (NER, classification, retrieval, summarization), partner with PMs and engineers to scope and ship features, and coach engineers on technical and career growth.

The role involves building standardized training frameworks, profiling model performance, collaborating with teams, and improving related tooling.

Lead design and production of next-generation search and personalization systems using LLMs, dense vector retrieval, GNNs, multi-stage ranking, and reinforcement learning. Drive GPU-based deployment (e.g., Triton), ensure <100ms latency, mentor applied scientists and MLEs, and partner cross-functionally to productionize scalable, reliable search and recommendation experiences.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

Build and maintain robotics data pipelines, curate and version large-scale sensor datasets, configure ML training workflows, optimize cloud costs, and develop tools for perception engineers. Responsibilities include dataset management, experiment reproducibility, automated evaluation, and metrics for dataset health, model performance, and pipeline reliability.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

As a Machine Learning Engineer, you will develop scalable ML systems, work with large datasets, and collaborate with a talented AI team.

Build self-service ML platform tooling and golden paths enabling Data Scientists to deploy and monitor models across batch and real-time. Design CI/CD, model registry, deployment workflows, observability standards, SDKs/CLIs, and partner with staff MLEs to shape platform architecture and developer experience.

Architect and deploy scalable machine learning and generative AI systems, including LLM, RAG, inference, evaluation, and LLMOps pipelines. Develop prompting, retrieval workflows, guardrails, schema validation, and production evaluation systems focused on accuracy, safety, reliability, and cost. Lead technical decisions, collaborate with product and engineering teams, drive greenfield initiatives, and mentor engineers building secure AI-powered experiences.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

As a Senior Machine Learning Engineer, you'll design and develop AI systems, focusing on LLMs and improving agent performance while mentoring engineers.

Design, build, and operate scalable backend services in Python or Go to power underwriting decisioning. Integrate statistical and ML models, implement APIs (REST, gRPC/GraphQL), ensure observability and testing, lead projects, and collaborate with product, data science, and underwriting teams to automate underwriting workflows.

Develop, train, deploy, and optimize machine learning models for research and production ultrasound applications. Build scalable ML data pipelines and tools, automate retraining, evaluation, and monitoring, and improve inference speed, robustness, and maintainability. Collaborate with researchers and software engineers to productionize ML research while reducing technical debt and communicating concepts to technical audiences.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Build and deploy applied machine learning models and data-driven systems that classify, cluster, label, and enrich Internet-observed assets. Develop feature pipelines, training datasets, evaluation frameworks, confidence scoring, and production inference services. Collaborate with engineering, research, security, and product teams to transform raw Internet telemetry into high-quality datasets and customer-facing insights.

Industry Expert who reviews and defends advanced AI, ML, distributed systems, and forward-deployed engineering projects. Responsibilities include challenging architecture decisions, chairing mock review boards and executive presentations, hosting one or two technical sessions monthly, and serving as the escalation point for instructors. The role requires production-scale engineering judgment, strong technical communication, and expertise in agentic systems, LLM evaluation, infrastructure, observability, model serving, RAG, or enterprise deployments.

Lead and own the ML/AI function: architect multi-modal agentic systems, define evaluation frameworks and datasets, productionize reliable scalable ML, ensure compliance, and build and mentor a high-performing ML team.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

The Senior Account Executive will develop relationships with utility clients, lead sales initiatives, negotiate contracts, and maintain sales activities in Hubspot to achieve sales targets.

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.