Top Machine Learning Jobs in Toronto

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.

The role involves designing and implementing MLOps pipelines, optimizing machine learning processes, and ensuring model performance and compliance in a collaborative environment.

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.

Lead design, development, and deployment of production ML and LLM systems for real-time rider applications. Architect scalable ML pipelines, collaborate cross-functionally, translate research into production, mentor engineers, and ensure high-quality code and data-driven solutions.

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

Design, build, and maintain scalable AI/ML pipelines for data processing, model training, and deployment. Collaborate with data scientists and engineers to implement and optimize AI solutions, troubleshoot pipeline issues, document best practices, and develop MVP code to transition into production-ready systems.

Design, build, and deploy predictive and generative ML models (including LLMs) for banking use cases. Create production-ready, modular ML pipelines, evaluate and monitor models, run A/B tests, and translate business problems into measurable solutions while collaborating cross-functionally.

As a Data Scientist at Mistplay, you'll design and optimize the rewards economy, implement machine learning systems, and analyze strategies to enhance user engagement and business outcomes.

Manage and grow strategic partnerships across US & Canada for a Google Cloud-focused AI and data consultancy. Drive co-sell initiatives, GTM planning, partner enablement, joint marketing, event execution, partner onboarding, and pipeline generation while tracking partner performance and reporting impact.

The Principal Product Manager will lead AI-native systems for pricing intelligence and financial integrity within the travel sector, collaborating with cross-functional teams to enhance revenue processing and model lifecycle management.

The Senior Forward Deployed Engineer will deliver technical solutions using AI, engage with customers, and lead project lifecycles while mentoring junior engineers.

Senior Data Scientist for Rovio's Beacon platform in Espoo. Develop predictive and statistical models for User Acquisition, handle long-term forecasting, uncertainty quantification, optimization, and rare-event behavior. Translate research into production, own model lifecycle from experimentation to deployment and monitoring, and collaborate closely with UA, game teams, finance, analysts, and engineers while contributing to a collaborative modelling culture.

As a Machine Learning Engineer at Doppel, you will design, train, and deploy models for detecting malicious content, working with large datasets and collaborating with teams to ensure scalability and production readiness.

Manage the vision, strategy, and roadmap for Contract Intelligence AI products. Collaborate with teams to define requirements and market strategies, ensuring delivery of generative AI solutions.

Lead end-to-end product design for Clio's Fintech/payments area: conduct customer research, frame opportunities, prototype and ship AI-enabled workflows, improve design systems, measure impact, and mentor other designers.

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 end-to-end product design for an AI-powered coaching platform: translate ambiguous concepts into cohesive UX, run usability testing, iterate from data and research, own the design system, define visual identity and interaction patterns, mentor designers, and validate AI-integrated interfaces inside real games.

Build scalable, reliable ML infrastructure and services for notebooks, training, experimentation, deployment, serving, and LLM applications. Partner with ML engineers and product teams to enable production-ready MLOps workflows, improve developer productivity, and operate high-availability, low-latency systems across regions.

Design, train, evaluate, optimize, and deploy computer vision deep learning models for real-time edge inference. Enhance Vertex AI/KFP pipelines, convert and optimize models to TFLite for edge, manage model artifact/versioning in GCS, and implement automated evaluation gates for safe deployments.

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

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.