Top Machine Learning Jobs in Montreal

Source, aggregate, and manage geospatial datasets (imagery, rasters, vectors, point clouds) and build automated tooling (GDAL, Python, in-house tools) to preprocess and postprocess data for AI/ML pipelines while partnering with AI/ML, pipeline, and product teams.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

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.

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.

Seeking a Senior C++ Programmer to develop ML applications, optimize performance, and collaborate with teams. Requires extensive C++ and AI/ML experience.

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.

Senior C++ programmer responsible for developing, integrating and optimizing ML inference engines and libraries for game engines; leverage NPU/GPU/CPU, implement tests, evaluate and optimize model performance, prototype and document, and collaborate with data scientists and game programmers.

Build and deploy agentic and generative AI systems that automate risk and compliance investigations. Own full-stack ML systems, including data integrations, prompt tuning, orchestration, evaluation, productionization, monitoring, and transformer-based modeling across transaction, graph, text, and profile data. Collaborate with product, engineering, platform, and operations teams while leading strategic roadmaps and delivering scalable production systems.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Develop and operationalize predictive models and segmentations using data from data warehouses. Perform feature engineering, model development, evaluation, and documentation. Present analyses and visualizations to stakeholders, support AI/advanced analytics mandates, and recommend improvements to data management and modeling processes.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

Design and develop AI tools for smart grid applications, integrate ML models into software, and optimize system performance.

Lead development and productionization of fraud prediction models (tabular, graph, behavioral). Build scalable feature pipelines, run experiments, integrate models into real-time/batch systems, monitor model and data health, and collaborate cross-functionally to maintain model reliability and performance.

Lead and mentor a team of applied ML scientists to design, build, and deliver predictive AI solutions (regression, classification, anomaly detection) across personal banking. Collaborate with engineers, research scientists (Layer 6), and stakeholders to translate business objectives into scalable models, ensure governance and compliance, monitor systems, and drive continuous improvement and innovation.

As a Staff Machine Learning Engineer, you'll lead AI initiatives using large-scale data, optimize ML models for edge devices, and collaborate with cross-functional teams.

Lead cross-functional teams of machine learning engineers and data scientists, driving technical direction and fostering a collaborative environment to develop high-quality ML-driven capabilities.

Developers will use AI-assisted tools to accelerate software delivery or design and deploy ML/LLM-based production solutions (LLMs, RAG, vector search, data pipelines). Work includes system integration, improving UX, and integrating models into clinical workflows. The role requires bilingual French/English communication and contributes directly to healthcare organization impact.

Build large-scale machine learning systems for advertising, including ranking, recommendation, targeting, prediction, optimization, and creative generation. Develop feedback loops connecting generative AI outputs to advertising performance signals, apply prompt engineering and post-training methods, run experiments, and ship reliable production ML systems. Collaborate across Product, Research, Engineering, and go-to-market teams to deliver AI-native advertising products.

The Applied Scientist will drive research on efficient adaptive machine learning, deploy ML systems end-to-end, and contribute to product strategy, focusing on real-world impact and innovation.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Build and ship end-to-end ML/AI systems (traditional ML, NLP, LLMs/AI agents) on transaction, merchant, and location data. Prototype, productionize, and monitor models, collaborate with engineering and product, and (for senior hires) shape roadmap and mentor teammates.