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.

Développe et maintient des systèmes, moteurs d’inférence, bibliothèques et frameworks de machine learning en C++. Optimise les calculs sur CPU, GPU et NPU, intègre les systèmes ML aux moteurs de jeu, conçoit des outils d’évaluation de performance et écrit des tests. Collabore avec les équipes de science des données, recherche et programmation, tout en réalisant des preuves de concept, de la recherche, de la documentation et du partage de connaissances.

Own and improve production machine learning pipelines focused on accuracy, efficiency, and trustworthiness. Build LLM evaluations, judges, datasets, metrics, and benchmarks; run experiments across prompts, models, and pipeline designs; and reduce LLM costs through smaller task-specific models. Expand anonymization capabilities, evaluate open-source models, and collaborate with quality engineers to ship reliable ML features. Document findings and communicate technical trade-offs clearly.

Develop AI/ML strategy and production software for a healthcare data platform. Design architectures, implement semantic matching, RAG, document parsing, agentic systems, knowledge graphs, and evaluation workflows. Write clean, tested, documented code; conduct reviews and pair programming; collaborate with clients and product teams; mentor developers; and contribute to technical strategy and Agile delivery.

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.

Lead the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.

Deploy Fastino language models into customer production environments by building integrations, data pipelines, evaluation systems, and services. Fine-tune and assess models for accuracy, latency, and cost; troubleshoot authentication, networking, GPU performance, inference, and data quality issues. Own technical delivery from initial discussions through launch, create reusable tools and SDK improvements, and relay customer feedback to research and product teams.

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.

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.

Develop high-performance machine learning inference runtimes and compiler components for NVIDIA platforms. Optimize neural network graph mappings through compiler transformations, scheduling, memory layouts, profiling, and benchmarking. Collaborate with hardware architects on codesign, evaluate spatial accelerator techniques, and contribute to NVIDIA’s software ecosystem. The role also involves research, technical publications, and presentations on compilation and inference optimization.

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.

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.

AI/ML engineering intern will help design, build, evaluate, and deploy LLM-powered applications and machine learning systems. Responsibilities include developing data pipelines, working with structured and unstructured data, supporting model training and optimization, preparing systems for production monitoring, and collaborating with engineers, researchers, and product teams. The role offers mentorship and hands-on experience applying AI to healthcare, government, and energy workflows.

Own the production deployment and optimization of machine learning models at high request volumes. Responsibilities include reducing inference latency, improving throughput, profiling GPU code, containerizing models, designing distributed multi-GPU serving systems, and applying quantization, batching, caching, compression, and knowledge-transfer techniques. The role requires strong systems programming, GPU optimization, distributed systems, container orchestration, and production software experience.

Develop and deploy supervised and unsupervised machine learning models for manufacturing applications, including anomaly detection, classification, and regression. Responsibilities include data preprocessing, model selection, optimization, scalability improvements using Dask, Ray, and Optuna, and collaboration with cross-functional teams. The role requires expertise in tree-based and neural network algorithms, custom loss functions, PyTorch, Python, and the full machine learning lifecycle.

Develop and validate machine learning models, training pipelines, inference systems, and numerical methods using Python. Debug algorithmic and system-level issues, optimize memory usage and performance, design systematic tests, and build reproducible workflows. Review challenging ML engineering tasks, use coding agents in development, document technical findings, and deliver high-quality work independently within deadlines.

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.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

Design and develop advanced machine learning and generative AI solutions, including LLM fine-tuning, RAG architectures, prompt engineering, model evaluation, and MLOps. Architect scalable training, deployment, and retraining workflows on AWS using SageMaker and related services. Collaborate with engineering, QA, project management, and other stakeholders to deliver reliable AI applications and solve complex business problems.

Designs, deploys, and maintains production machine learning systems for identity resolution, scoring, deliverability, and personalization. Builds batch and real-time feature pipelines, develops classification models, integrates ML with graph databases, and manages monitoring, drift detection, retraining, and performance reporting. Partners with engineering to deliver low-latency APIs and scalable services while contributing to GPU-accelerated processing, MLOps standards, and large-scale graph computation.