Top Machine Learning Jobs in Toronto

Leads the architecture, development, deployment, scalability, reliability, and operational excellence of enterprise AI and machine learning systems. Partners with researchers to productionize LLMs, agentic AI, and responsible AI capabilities; establishes MLOps and LLMOps practices; oversees observability, incident response, performance, security, and governance; and mentors engineers while providing senior technical leadership.

Develop secure, scalable, production-ready AI applications, services, and APIs. Integrate machine-learning and generative AI models into enterprise systems, build data and retrieval workflows, develop prompts, and implement testing, monitoring, logging, and evaluation. Collaborate with product, data, platform, and engineering teams while applying security, privacy, responsible-AI, and governance practices. Troubleshoot issues, document technical designs, participate in code reviews and Agile ceremonies, and support continuous improvement.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Conduct applied AI/ML research focused on time-series forecasting and generative AI for supply chain planning. Review literature, formulate hypotheses, design reproducible experiments, develop and evaluate models, analyze results, and communicate findings. Build research prototypes and collaborate with researchers, engineers, data scientists, and domain experts to assess product potential. Contribute to technical reports, demonstrations, publications, and patents under mentorship.

Supports responsible AI governance by developing policies, maintaining control frameworks, conducting AI risk assessments, monitoring regulatory compliance, preparing reports, and advising legal, compliance, security, AI, and business stakeholders. The role also tracks governance metrics, supports audits, researches emerging regulations, and recommends improvements to AI governance programs.

Monitors and improves the performance, reliability, and adoption of AI-powered systems. Analyzes operational metrics, develops dashboards and reports, identifies bottlenecks, supports incident resolution, documents procedures, and recommends process improvements. The role collaborates with AI engineers, product teams, business stakeholders, and operations leaders while supporting governance, compliance, risk mitigation, and continuous improvement initiatives.

Leads the strategy, roadmap, development, launch, and optimization of AI-powered products. Defines product requirements, priorities, success metrics, and business outcomes while coordinating engineering, AI, executives, and business stakeholders. Evaluates emerging AI technologies, supports responsible AI governance, manages feedback and adoption, and drives continuous product improvement.

Leads AI data annotation operations, managing workflows, resources, productivity, quality assurance, and project timelines. Coaches and develops annotation teams, supports recruitment, and collaborates with AI engineers, trainers, and product teams. Establishes annotation guidelines, conducts audits, resolves quality issues, and drives process improvements, automation, tool adoption, and operational excellence across AI data initiatives.

Develop and manage the strategy, roadmap, lifecycle, and delivery of AI-powered products. Translate business needs into product requirements, collaborate with engineering and AI teams, define success metrics, monitor adoption and ROI, and drive continuous improvements. The role also engages customers and stakeholders, evaluates emerging technologies, supports governance and compliance, and promotes responsible AI practices.

Own AI product backlogs, priorities, user stories, acceptance criteria, and delivery outcomes. Translate business needs into product requirements, collaborate with technical teams on solution design, facilitate stakeholder communication, support Agile delivery, and monitor product performance and adoption. The role also identifies AI-driven improvements, supports product releases, drives continuous improvement, and promotes responsible AI practices.

Leads business analysis for AI and automation initiatives by identifying adoption opportunities, developing business cases, gathering requirements, facilitating stakeholder workshops, supporting solution design and delivery, measuring benefits, and contributing to AI governance and continuous improvement.

Supports responsible AI governance by developing frameworks, conducting risk and impact assessments, evaluating fairness and transparency, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight processes. Collaborates with technical, legal, compliance, risk, and business teams to promote ethical, accountable, and trustworthy AI deployment.

Translates business needs into AI solution requirements, assesses AI and automation opportunities, develops business cases, and supports solution design and delivery. The role facilitates stakeholder collaboration, testing, implementation, change management, reporting, benefits tracking, and continuous improvement across AI initiatives.

The AI Security Analyst assesses and mitigates risks across AI systems, machine learning platforms, and Generative AI applications. Responsibilities include threat modeling, vulnerability identification, security monitoring, incident response, root cause analysis, access control, policy development, compliance support, audit participation, and secure design reviews. The role partners with engineering, infrastructure, governance, and business teams to improve AI security capabilities and promote responsible AI adoption.

Leads operational oversight, performance analysis, governance, and continuous improvement for AI-powered products and platforms. Develops performance frameworks, executive dashboards, business insights, forecasts, and risk reporting. Partners with engineering, product, and operations teams to improve workflows, support automation, ensure compliance, and drive scalable AI operating models.

Tests and validates AI-powered products, machine learning models, and Generative AI applications. Designs test strategies, executes functional and regression testing, evaluates model outputs, identifies defects, analyzes quality metrics, documents results, verifies compliance, and collaborates with AI engineers and product managers to improve testing practices and release readiness.

Drive end-to-end delivery of Autodesk’s Generative AI Accelerator, moving AI capabilities from research through production and adoption. Coordinate research, engineering, security, legal, product, and platform teams; manage plans, dependencies, risks, forecasting, reporting, and impediment resolution. Partner with executive stakeholders to shape strategy and sequencing, promote Agile and Lean practices, and support responsible use of AI automation. Requires technical fluency in AI/ML, cloud platforms, distributed systems, and software development lifecycles.

Architects, develops, and deploys high-performance machine learning systems across the full ML lifecycle. Responsibilities include building large-scale data and feature pipelines, training tree-based and deep learning models, developing low-latency inference services, implementing MLOps and monitoring, and designing rigorous experimentation. The role requires strong Python and software engineering expertise, distributed computing experience, production ML serving skills, and familiarity with statistical evaluation and A/B testing.

Consultative Sales Engineer responsible for driving complex sales opportunities, designing cloud data security and data risk management solutions, delivering proof-of-value engagements and technical demonstrations, advising executive stakeholders, supporting partner channels, and coordinating cross-functional resources. The role requires cloud security, IAM, networking, databases, encryption, privacy, data governance, and strong technical communication skills.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Design and operate production backend services and AI infrastructure, including model serving, inference, data pipelines, vector stores, RAG systems, APIs, and microservices. Architect scalable real-time and batch systems, integrate and deploy LLM, computer vision, and machine learning models, and ensure reliability, observability, testing, and CI/CD. Collaborate with ML, product, and customer-facing teams while documenting systems and mentoring engineers.

Conduct legal research and analysis to support AI-driven legal technology solutions. Create and maintain training data, perform relevance evaluations, provide actionable feedback to data scientists, engineers, and product managers, and contribute to stakeholder presentations. The role also supports responsible AI initiatives and executes projects with increasing independence.

Lead exploration and application of large language models and generative AI for enterprise contact-center automation. Translate research into high-performing, deployable ML systems; contribute across experimentation, implementation, and deployment; collaborate with engineering and product teams; optimize performance; and help shape team strategy, innovation, and professional growth.

Lead exploration and application of large language models and generative AI, translate research into deployable ML systems, and guide strategic direction. Develop solutions across experimentation, implementation, and deployment while collaborating with engineering and product teams to optimize performance and customer outcomes in contact center automation.

As an Applied Scientist, you will push the limits of AI/ML in home valuation, tackling diverse ML challenges and optimizing decision-making with data.