Top Machine Learning Jobs in Montreal

Own the direction and implementation of Babylist’s personalization domain, including recommendation, search, feed models, custom embeddings, and shared representations. Take ambiguous problems from concept through production, owning architecture, deployment, monitoring, retraining, evaluation, and customer impact. Partner with product, design, and data, establish AI development standards, make cross-team technical decisions, and coach senior engineers while remaining hands-on with complex machine learning systems.

Design and operate large-scale data and ML platforms supporting audience data, feature stores, training pipelines, model serving, monitoring, and LLM-powered workflows. Own end-to-end data pipelines, reliability, SLAs, observability, and business impact. Apply distributed systems principles, production ML practices, and agentic technologies while collaborating across product and engineering teams in a fully remote environment.

Develop and maintain C++ machine learning systems, inference engines, and ML frameworks for integration into game engines and games. Optimize AI workloads across NPU, GPU, and CPU hardware; evaluate model performance; write tests; and collaborate with data science, research, gameplay, and production teams. Responsibilities also include proof-of-concepts, model training and fine-tuning, technical research, system improvements, documentation, and knowledge sharing.

Développer, intégrer et maintenir des systèmes, moteurs d’inférence, bibliothèques et frameworks de machine learning en C++. Optimiser les performances sur CPU, GPU et NPU, intégrer les systèmes ML aux moteurs de jeu, écrire des tests et collaborer avec les équipes de données, recherche et programmation. Le rôle comprend également la création de preuves de concept, l’expérimentation de modèles, la recherche, la documentation et le partage de connaissances.

Design and lead end-to-end architectures for production machine-learning systems at extraordinary scale. Responsibilities span data preparation, feature generation, model delivery, deployment, hosting, inference, monitoring, performance optimization, and technical design. The role establishes engineering standards, influences ML architecture, and collaborates across data science and software engineering to build reliable, maintainable systems for high-volume, low-latency workloads.

Design and develop machine-learning approaches for vehicle control, including learned vehicle dynamics models, adaptive controllers, data pipelines, evaluation metrics, and simulation tooling. Integrate learned models into safety-critical closed-loop systems and validate algorithms through offline experimentation, simulation, and on-vehicle testing. Collaborate with engineers and research scientists to combine classical control with AI for scalable self-driving technologies.

Paid 6–12 month research internship developing and experimenting with ML models (vision, NLP, speech) for game agents, player safety, content generation, and developer tools. Tasks include data creation/analysis, end-to-end model design and prototyping, and collaborating with research scientists and engineers to deploy models.

Develop and deploy deep learning and computer vision models for on-device analytics. Responsibilities include designing neural networks, optimizing real-time inference for resource-constrained platforms, managing the model lifecycle from data acquisition through deployment, developing training and deployment tools, evaluating new technologies, and building prototypes for future products.

As a Staff ML Engineer, you'll design and operate Samsara's ML platform, partnering with teams to deliver scalable ML solutions that enhance safety and efficiency in physical operations.

Lead the underwriting and credit risk validation sector within Model Risk Management. Manage and mentor a quantitative team, perform hands-on validations of complex ML models, assess data integrity and model performance, identify and remediate weaknesses, and partner with stakeholders to enhance the enterprise MRM framework and meet audit and regulatory requirements.

Lead applied AI and machine learning innovation for supply chain products. Develop prototypes and proof-of-concepts, evaluate emerging techniques, and guide promising ideas toward scalable enterprise capabilities. Provide technical leadership across research, product, and engineering teams while remaining hands-on with ML, generative AI, agents, LLMs, optimization, recommendation, and explainability. Influence architecture and technical direction, mentor colleagues, assess AI-generated code, and ensure solutions meet quality, security, maintainability, and customer-impact standards.

Conçoit, développe, déploie et exploite des modèles de machine learning et d’IA générative en production. Construit des pipelines d’entraînement, validation, inférence et CI/CD, soutient les architectures LLM et RAG, surveille la performance et la dérive des systèmes, optimise les coûts et la latence, et veille à la sécurité, à la confidentialité et à l’IA responsable. Collabore avec les équipes d’architecture, de développement et de données, tout en encadrant des ingénieurs juniors.

Build and deploy conversational AI systems, production ML models, evaluation frameworks, simulations, CI quality gates, and ML platform tooling. The role includes model serving, LLM infrastructure, observability, and real-time voice or SMS AI applications. Responsibilities also include technical design, code reviews, documentation, mentorship, production ownership, and improving AI development practices across teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

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.

Build and maintain Quora’s machine learning platform and distributed model-serving infrastructure. Develop scalable systems for recommendation and language models, optimize GPU serving latency, throughput, and cost, improve ML tooling and feature stores, and contribute to platform modernization. The role includes production deployment, collaboration with senior engineers, mentoring, and participation in an on-call rotation.

Develop and deploy machine learning models using complex signal, image, spectral, and sensor datasets. Build reproducible ML pipelines, APIs, backend services, and CI/CD workflows on Azure. Monitor model performance, drift, latency, reliability, and costs in production. Apply research to real-world R&D problems, optimize large-scale data processing, and collaborate with clients, domain experts, engineers, and laboratory teams.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

Develop and improve machine learning models for motion prediction and media applications. Responsibilities include reviewing ML research, building experimental pipelines and prototypes, processing large-scale data, training and tuning models, analyzing errors, developing scalable workflows, and collaborating on global research projects. The role requires strong deep learning, statistical ML, programming, algorithms, infrastructure, and software engineering expertise.

Design and scale AI agent systems in Unity for goal-directed behavior, planning, decision-making, and persistent world modeling. Develop reusable agentic frameworks and ML models, optimize real-time performance, and lead reinforcement learning, NPC planning, and agent memory initiatives. Establish model evaluation and AI safety practices, collaborate across teams, contribute to product strategy, and mentor engineers and researchers.

Design, validate, and deploy machine learning solutions (prediction, segmentation, optimization, automation). Transform complex data into actionable insights, maintain data mining tools and platforms, collaborate cross-functionally, ensure analytical quality, and incorporate generative AI/LLMs for automation and information access.

Lead development and deployment of machine learning solutions (prediction, segmentation, optimization, automation). Transform complex data into actionable insights, maintain data tools/platforms, apply rigor in validation and monitoring, collaborate cross-functionally, mentor junior team members, and provide technical leadership including work with generative AI and LLMs to deliver production solutions.

Develop and optimize distributed machine learning training and inference systems for very large models. Collaborate with research scientists, diagnose performance bottlenecks, profile and debug experiment code, build orchestration tools, optimize compute resources, and establish best practices for large-scale ML workflows.