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.

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.

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.

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.

Design and develop quantitative applications, models and data tools to support private markets investment decisions. Apply machine learning, deep learning and NLP, integrate generative AI and maintain data ingestion/processing pipelines, ensure data quality, collaborate with investment teams and implement analytics into decision processes.

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.

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.

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.

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 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.

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

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.

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.

The Senior ML Programmer will develop ML tools, collaborate with various stakeholders, maintain ML systems, and ensure performance standards while also conducting research and documentation.

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.

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.

As a Machine Learning Engineer II at Affirm, you'll develop AI systems for automating customer operations and collaborate on building models while ensuring high-quality code production.

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.

As a Founding AI Engineer, you will develop an AI-powered knowledge platform, focusing on backend systems, machine learning data pipelines, and optimizing tech stack for performance while participating in the entire product lifecycle.

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

As a Software Engineering Intern, you will contribute to building features for user-facing products, APIs, and scalable services for machine learning. Responsibilities include crafting code, designing data pipelines, implementing tools, and enhancing technical documentation.

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.

Build, optimize, and embed machine learning models for on-device inference within a Go-based detection engine. Own models end-to-end: deploy, monitor performance, detect drift, iterate, and resolve production issues while collaborating with systems engineers and stakeholders.