Top Machine Learning Jobs in Montreal

Develop and scale production machine learning models for credit decisioning, including default prediction and approval decisions. Build training, decisioning, monitoring systems, data pipelines, features, and algorithms. Conduct research, develop prototypes, and collaborate with engineering, credit, and product teams to deliver machine learning products from inception through business impact.

Conçoit, développe et déploie des solutions d’intelligence artificielle en production. Traduit les besoins d’affaires en architectures, pipelines d’apprentissage automatique, API et services ML robustes. Met en place la gouvernance, le MLOps, la surveillance, la traçabilité, la sécurité et la conformité des modèles. Collabore avec les équipes produit, BI, science des données et DevOps, documente les décisions techniques et accompagne l’évolution continue des solutions.

Develop C++ machine learning systems, inference engines, libraries, and frameworks for integration into game engines and games. Optimize ML workloads across NPU, GPU, and CPU hardware; build model evaluation and performance systems; write unit and integration tests; and collaborate with data science, research, gameplay, AI, and production teams. Responsibilities also include proof-of-concept development, model experimentation, technical research, documentation, presentations, and knowledge sharing.

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.

Leads the technical direction and execution of a machine learning team focused on personalization, relevance, ranking, search, and advertising. Designs, scales, and operates production ML systems, including training, evaluation, deployment, and serving infrastructure. Partners with product, data, and engineering stakeholders, drives architecture and system design decisions, mentors engineers, and uses AI tools to accelerate development and explore LLM-powered features.

Leads the technical direction of a machine learning team focused on personalization, relevance, ranking, search, and advertising systems. Designs and scales end-to-end ML architecture, including training, evaluation, deployment, and serving. Partners with product, data, and engineering stakeholders, drives system design tradeoffs, mentors engineers, and ensures reliable production operation at consumer scale. The role also encourages using AI and LLM tools to accelerate development and explore new ML-powered features.

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 data pipelines, build and deploy machine learning and LLM-based NLP models, evaluate model performance through experiments, and optimize or fine-tune existing models. The role requires experience with TensorFlow or PyTorch, CI/CD, MLOps, Linux, SQL, Git, Bash scripting, and Python.

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.

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.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

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, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

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.