Top Machine Learning Jobs in Montreal

Lead the design and implementation of AI products for edge devices, optimizing machine learning models under edge-compute constraints. Work with petabyte-scale sensor, video, text, diagnostic, transaction, and location data; collaborate with ML, firmware, hardware, and full-stack engineers; prototype new AI experiences; ship production systems at scale; and mentor ML engineers.

Designs, builds, and operates shared machine learning infrastructure, including feature stores, model hosting, inference, training, evaluation, and rules engines. Leads large-scale distributed systems, reliability improvements, incident response, and safe deployments. Partners with applied ML teams to develop reusable platform capabilities, supports internal users, mentors engineers, and contributes to technical roadmaps and design reviews.

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.

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.

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.

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.

Build and scale infrastructure for real-world AI evaluation, including data pipelines, model training, inference APIs, and benchmarking systems. Own full-stack data, model, and evaluation pipelines; research state-of-the-art evaluation methods; improve reproducibility and support new modalities. Collaborate with researchers, engineers, and product leaders to turn ideas into reliable, scalable products for assessing frontier AI models.

Build and deploy production machine learning systems across the full ML lifecycle, including dataset creation, research, experimentation, model optimization, deployment, monitoring, and LLM applications. Develop novel solutions, manage rapid prototyping pipelines, track experiments, optimize inference performance, and apply techniques such as quantization, pruning, fine-tuning, prompt engineering, and RAG. Collaborate in a fast-paced healthtech environment focused on improving post-acute care.

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.

Leads the machine learning organization responsible for identity resolution, fraud and abuse detection, risk scoring, and consumer intelligence. Owns ML strategy, team development, model performance, platform infrastructure, deployment, monitoring, and production reliability. Partners with product, engineering, security, customer success, and AI teams while managing data partnerships and executive communication. The role requires extensive engineering and ML leadership experience, production experience with consequential automated decisions, and expertise in adversarial or large-scale ML systems.

Build and fine-tune foundation, ranking, CTR, conversion, and engagement models. Own the complete model lifecycle from research and experimentation through training, evaluation, deployment, monitoring, and continuous improvement. Develop scalable ML infrastructure, feedback loops, and evaluation systems tied to customer outcomes. Collaborate with technical and non-technical teams to translate business goals into modeling objectives and product improvements.

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 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.

Lead and manage the ML Dataset Platform team while defining its technical direction, roadmap, APIs, tooling, storage formats, versioning, lineage, and data-loading infrastructure. Build scalable systems for petabyte-scale multimodal datasets, optimize training data access across clouds, partner with ML platform and model teams, hire and mentor engineers, and drive platform adoption across the organization.

Leads the architecture, development, deployment, and optimization of enterprise AI/ML systems, including LLMs and generative AI. Provides technical leadership and mentorship, operationalizes models, implements MLOps, and integrates solutions across AWS, GCP, Azure, Docker, and Kubernetes. Partners with executives, clients, product managers, data scientists, and engineering teams to align AI roadmaps with business goals while ensuring scalable, monitored, cost-efficient, and production-ready systems.

Train and operate machine learning models on Amazon SageMaker using AWS Trainium. Develop and optimize PyTorch training code for NeuronCore hardware, diagnose device and compiler issues, tune distributed workloads for throughput and cost, and deliver production training pipelines while collaborating directly with client and engineering teams.

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.

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.