Top Machine Learning Jobs in Toronto

Build and standardize ML and AI platform patterns to move models from experimentation to production. Implement MLOps/LLMOps practices (Databricks, AWS, MLflow), deployment templates, monitoring, evaluation, governance, and secure serving. Partner with Data Engineering and Governance to version, monitor, and govern models, prompts, agents, and data products while managing cost, performance, reliability, and operational risk.

As a Machine Learning Intern, you will assist in developing, training, and deploying machine learning models, collaborating with data scientists.

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.

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.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

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.

Build and operate production-grade machine learning and Generative AI platforms, pipelines, and reusable MLOps patterns. Responsibilities include CI/CD, Terraform infrastructure, secure credential management, scalable model serving, data pipeline optimization, model development and deployment, monitoring, governance, and responsible AI. The role collaborates with data scientists, engineers, and business stakeholders across global insurance, banking, and wealth management operations.

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.

Lead hands-on design, prototype, and deploy end-to-end AI/ML and Generative AI solutions (LLMs, RAG, agents). Build MLOps/LLMOps pipelines, integrate vector DBs, ensure observability and governance, collaborate cross-functionally, and mentor engineers to move projects from prototype to production.

Lead development of ML systems for retailer acquisition and engagement: bidding optimization, targeting, AEO/LLM-driven content, causal inference experiments, and scalable marketplace solutions working cross-functionally.

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

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

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 VP of Data & AI Engineering leads Duck Creek's enterprise data and AI strategy, overseeing global teams, implementing scalable platforms, and ensuring data governance and responsible AI practices.

The Director of Engineering will lead and scale Buzz Solutions' engineering organization, focusing on AI, infrastructure, and platform systems while ensuring best practices and high-quality solutions.

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.

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.

Lead product management for AI tools in software/SaaS, manage product lifecycle, drive strategic roadmap, and ensure product adoption through iterative shipping and user feedback.

Bay Area-based hunter Enterprise Account Executive responsible for prospecting, developing, and closing new enterprise business for Anyscale. Manage complex sales cycles, achieve quotas, lead CxO engagements, qualify opportunities, collaborate with marketing, and drive ongoing account growth while advising customers on ML, compute, and cloud solutions.

Define and build Wave's internal risk and payments operations platform. Own strategy and roadmap for tools used by Risk, Fraud, Compliance, Support, Finance, and Payments Operations. Replace manual workflows with scalable decisioning, case management, and automation. Partner cross-functionally with Engineering, Data, and business teams to improve efficiency, controls, and customer outcomes.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

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

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.