Top Machine Learning Jobs in Montreal

Develop secure, production-ready software and machine learning systems for client environments. Translate complex business workflows into technical solutions, build and test full-stack applications and AI models, and collaborate closely with consultants, clients, and business users. Responsibilities include owning AI-focused products and projects, applying LLMs and machine learning, improving existing systems, identifying business opportunities, and staying current with emerging AI technologies.

Leads a team of actuaries and data scientists delivering machine learning and analytical solutions for property and casualty insurance pricing. Sets the strategic direction for end-to-end modeling, oversees scalable frameworks and inference pipelines, partners with stakeholders and model governance teams, ensures regulatory compliance, and mentors team members across technical and business disciplines.

Lead the evaluation, training, fine-tuning, and production integration of machine learning and large language models. Build prompt engineering and model evaluation pipelines, optimize Elasticsearch-backed AI workflows, benchmark commercial and open-source models, and monitor production performance. Collaborate with product, engineering, and security teams to deliver safe, scalable AI capabilities while identifying improvements to data pipelines and engineering processes.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

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, implement, evaluate, and deploy production-ready machine learning models for game bots, intelligent NPCs, companions, world modeling, moderation, security, and QA automation. The role applies deep learning, computer vision, language models, vision-language-action models, and reinforcement learning, while assessing data channels and contributing to the machine learning roadmap.

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.

Graduate-level intern will research and prototype AI, machine-learning, and robotics approaches for autonomous heavy equipment in Vortex Studio. Responsibilities include integrating AI and robotics technologies, training and evaluating autonomous agents, using demonstrations and human feedback, developing sim-to-real workflows, building quantitative experiments, and documenting findings. The role focuses on embodied AI, realistic physics-based simulation, and autonomy for excavators, bulldozers, and wheel loaders.

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.

Design, develop, and evaluate large-scale multimodal foundation models for biological research. Build hybrid LLM and graph neural network architectures, relational foundation models, and distributed multi-GPU training systems. Apply deep learning theory to biological data, transition research prototypes into scalable production systems, publish peer-reviewed research, and mentor junior staff while collaborating across scientific and engineering teams.

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 technical direction and hands-on development of ML and operations research systems powering delivery dispatch. Build pricing, quote selection, forecasting, routing, batching, fleet recommendation, LLM, optimization, evaluation, and MLOps capabilities. Translate operational constraints into reliable models and solver formulations, establish production ML standards, and mentor engineers. The role partners with Product and leadership to deliver measurable improvements in revenue, margin, utilization, conversion, and delivery reliability.

Design and build production agentic AI systems for enterprise clients. Develop autonomous agents using Glean, OpenAI/GPT Enterprise, and AWS or GCP. Own agent lifecycles from architecture and implementation through deployment, monitoring, evaluation, and iteration. Apply tool calling, RAG, multi-step orchestration, memory, prompt engineering, structured outputs, permission-aware retrieval, and observability to create reliable, secure, maintainable enterprise-scale AI solutions.

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.

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.