Top Machine Learning Jobs in Toronto

The Enterprise Architect leads the development of AI and Data architecture, aligning technology with business needs while ensuring adherence to governance standards. They provide strategic guidance, collaborate with stakeholders, and utilize advanced technologies to enhance solutions and processes.

Develop, deploy, and own RTL-to-GDSII physical design methodology and CAD flows for IP delivery. Automate PPA exploration, document flows, partner with design teams and EDA vendors, qualify new nodes/PDKs and EDA releases, and apply ML/LLMs where they improve flow optimization and scalability.

Lead roadmap and execution for ad-serving, bidding, targeting, auction mechanics, and core marketplace infrastructure. Apply AI/ML and generative models to improve ROAS, budget utilization, and advertiser outcomes, partnering with engineering, analytics, commercial, finance, and legal. Drive experimentation, A/B testing, and data-driven product decisions.

Design, develop, and deploy large-scale recommender systems and ML models (including LLMs) for personalized retrieval and ranking. Build and operate big data pipelines, production MLOps, and cloud-based infrastructure to serve real-time recommendations and drive marketplace and advertising revenue.

Own post-sale relationships with electric utility customers, drive adoption and ROI of the Buzz Solutions platform, identify expansion and at-risk accounts, coordinate training and enablement, advocate customer needs to product and engineering, and maintain account health data in Salesforce.

The Software Engineer, Perception develops autonomous aircraft systems to detect and manage wildfires using real-time data processing and machine learning techniques.

Provide legal support for commercialization and product development of an AI-powered ad tech platform: draft and negotiate commercial agreements, manage IP portfolio and complex IP litigation, advise on AI, data, and privacy matters, counsel product teams on compliance and IP protection, and develop automated legal workflows.

Senior pre-sales engineer partnering with sales and channel partners to run technical demos, lead POCs, design solutions, and close large deals. Present to executives and technical audiences, build cloud ML/generative AI solutions, and advise on data architecture and Snowflake positioning.

Analyze player and business data to improve engagement, retention, and monetization. Build models, forecasts, dashboards, and A/B tests; synthesize qualitative feedback; partner with cross-functional teams; ensure tracking accuracy; and leverage AI tools to accelerate insights.

Develop data-analysis pipelines bridging ultrasound, synthetic biology, imaging and sequencing. Collaborate with wet-lab and ML teams to define objectives, encode priors, run ML pipelines for de-novo design and closed-loop active learning, and produce actionable insights to accelerate molecule discovery and device optimization.

Build and run ODAIA's legal function: draft, negotiate, and close enterprise SaaS, data licensing, MSA and DPA agreements; translate AI governance into internal policy; architect privacy and data-governance across PIPEDA/HIPAA; manage equity, board resolutions, vendor agreements; partner with Sales and Engineering to enable fast, compliant contracting and advise the executive team and Board on legal and risk matters.

Design and build distributed backend systems, microservices, and near-real-time data ingestion and processing. Scale an incrementality experimentation platform, add AI-powered workflows, extend data integrations and partner exports, and contribute frontend components as needed to deliver end-to-end product features.

As a Founding AI Engineer, you will develop an AI-powered knowledge platform, focusing on backend systems, machine learning data pipelines, and optimizing tech stack for performance while participating in the entire product lifecycle.

As an Automation Engineer, you'll build automation pipelines, collaborate on experimental needs, program instrumentation, and integrate with data science, enhancing research capabilities.

As a Social Media Intern at Inception, you will manage social content across platforms, engage with AI discussions, participate in product launches, and create engaging video content. Aimed at students with a passion for AI and strong writing skills, the role offers direct access to the founding team and the potential for full-time conversion.

Lead and scale Mimica's product organisation, define global product strategy and roadmap for AI-driven task/process mining and automation, mentor product and design teams, improve product operations and discovery, align cross-functional stakeholders, handle escalations, and hire and forecast team needs.

As a Senior Impact Analyst, you will analyze sustainability challenges, design and manage interventions, and work closely with smallholder communities, emphasizing rigorous research and collaboration.

The Senior B2B Marketer will develop and execute marketing strategies, collaborate with Sales, create marketing materials, conduct research, and optimize marketing effectiveness to drive growth in the gaming and 3D industries.

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.

Develop and optimize auto-labelling systems using ML and computer vision, manage data pipelines, and collaborate with researchers.

Build, test, and maintain software across full-stack, backend, infrastructure, data pipelines, and ML infrastructure to support autonomous trucking. Develop web interfaces, backend services, APIs, and tooling; improve reliability, observability, CI/CD, and deployment; participate in code reviews, debugging, and cross-team collaboration while learning new technologies.

Lead and execute technology audits across cybersecurity, cloud, IAM, SDLC, data/privacy, operational resilience, and AI governance. Plan fieldwork, test ITGCs and application controls against frameworks, document findings, manage remediation, and coordinate with stakeholders and co-sourced teams to improve control environments.

Lead and grow a team of 5–6 product developers to deliver high-quality, reliable software. Partner with product and design, champion CI/CD, testing, observability, and provide visibility to leadership while hiring and mentoring engineers.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Build and iterate underwriting ML models for real-time transaction decisions. Develop feature pipelines and training datasets, prototype and evaluate models, productionize into batch or real-time systems, and instrument model/data health, retraining, and monitoring. Collaborate with engineering, risk, product, and ML platform teams to deploy robust, low-latency solutions with appropriate risk controls.