Top Machine Learning Jobs in Montreal

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

Design and implement high-performance C++ systems for machine learning, computer vision, and 3D/procedural modeling. Apply research, optimize code for speed and scalability, collaborate with leadership, use Git, deploy and test on cloud with large-scale geospatial datasets, and operate in a Unix shell environment.

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.

Lead a team of ML engineers to develop and improve fraud detection models, defining strategies and fostering collaboration across functions to ensure effective model integration.

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.

Manage a team of ML engineers to develop machine learning solutions for underwriting applications. Set technical strategy, mentor engineers, and collaborate with teams across the organization.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Design, develop, and deploy machine learning models and analytics to improve customer acquisition, retention, and marketing. Perform EDA, feature engineering, model development, deployment, and monitoring. Collaborate with cross-functional teams, communicate findings, and build scalable, interpretable data products, including agentic AI solutions.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

Lead the development of fraud prediction models, build feature pipelines, prototype modeling ideas, and productionize models while collaborating with cross-functional teams.

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

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

Lead the development and improvement of fraud prediction models using machine learning techniques, collaborating with various teams to ensure model effectiveness and operational robustness.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Develop and enhance machine learning systems for fraud detection, build pipelines, prototype models, ensure model health, and collaborate with cross-functional teams.

The Senior Staff Machine Learning Engineer will lead the design and scaling of advanced ML systems, mentor engineers, and define technical strategy across Affirm.

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 the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

Lead Vegetation Intelligence product, owning roadmap from ML detection models to customer-facing risk products. Advance detection models, integrate outputs into outage and ignition risk, run user research, and translate model capability into actionable B2B products used in the field.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

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.

The role involves designing and deploying strategies for congestion revenue rights, applying quantitative methods, and developing machine learning models.