Top Machine Learning Jobs in Montreal

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.

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.

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.

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.

Develop and deploy machine learning models using complex signal, image, spectral, and sensor datasets. Build reproducible ML pipelines, APIs, backend services, and CI/CD workflows on Azure. Monitor model performance, drift, latency, reliability, and costs in production. Apply research to real-world R&D problems, optimize large-scale data processing, and collaborate with clients, domain experts, engineers, and laboratory teams.

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

Develop and improve machine learning models for motion prediction and media applications. Responsibilities include reviewing ML research, building experimental pipelines and prototypes, processing large-scale data, training and tuning models, analyzing errors, developing scalable workflows, and collaborating on global research projects. The role requires strong deep learning, statistical ML, programming, algorithms, infrastructure, and software engineering expertise.

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.

Lead the technical direction of Ubisoft’s internal DocGPT AI platform, focusing on retrieval pipelines, semantic indexing, vector search, reranking, LLM integration, evaluation, prompt engineering, and multi-tenant architecture. Identify architectural improvements, establish quality standards, mentor ML team members, and advise stakeholders. The role requires production experience in applied machine learning, information retrieval or NLP, RAG systems, cloud ML environments, and technical leadership.

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.

Develop and optimize distributed machine learning training and inference systems for very large models. Collaborate with research scientists, diagnose performance bottlenecks, profile and debug experiment code, build orchestration tools, optimize compute resources, and establish best practices for large-scale ML workflows.

Develop and optimize machine learning research workflows for large-scale model training and inference. Collaborate with research scientists to implement novel models and simulated environments, build tools that improve computing efficiency, and establish best practices for ML development. The role requires advanced ML expertise, deep learning research experience, high-performance computing, cloud platforms, containerization, and data infrastructure familiarity.

Develop and scale web-scale data pipelines that transform raw text into high-quality machine learning datasets. Responsibilities include deduplication, model-based scoring, filtering, toxicity and PII removal, metadata extraction, dataset versioning, provenance tracking, compliance, leakage detection, quality monitoring, and researcher tooling. The role also involves acquiring and evaluating text corpora, collaborating with research, engineering, legal, and governance teams, and developing novel data transformations for AI training.

Develop AI/ML strategy and production software for Smile Digital Health’s healthcare data platform. Design architectures and implement semantic matching, RAG, document parsing, agentic systems, knowledge graphs, and evaluation workflows. Write clean, tested, documented code, integrate external components, conduct reviews, collaborate with clients and product teams, contribute to technical strategy, and mentor developers in an Agile environment.

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.

Own and improve 2D, 3D, and fusion-based image segmentation models for clinical ultrasound and CT analysis. Build training, evaluation, dataset, and labeling pipelines; collaborate with clinicians, contractors, and data teams; and productionize auditable, low-latency inference services with confidence scoring, drift monitoring, and safe fallbacks. Work under medical-device design controls and support robust modeling across diverse patient anatomy and imaging conditions.

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.

Train and operate production machine learning models on Amazon SageMaker using AWS Trainium. Optimize PyTorch code for Trainium compilation, memory, throughput, and distributed training; diagnose hardware, compiler, device, data, and code issues; build cost-aware end-to-end training pipelines; and collaborate directly with client and internal engineering teams to deliver production workloads.

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.

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.