Top AI & Machine Learning Jobs in Montréal, QC

Design and maintain high-fidelity 2D/3D simulation environments, engineer reward functions, implement and optimize RL algorithms (PPO, SAC, Offline RL), coordinate cross-functionally, and develop sim-to-real strategies (domain randomization/adaptation) to ensure autonomous agents perform reliably in real-world multi-sensor settings.

Design high-level systems that integrate ML models into production, enable ML–rendering engine interaction, build language-agnostic APIs/wrappers and microservices, evaluate build-vs-buy options, coordinate cross-functional integration requirements, and decompose product vision into scalable ML-enabled architecture.

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.

Source, aggregate, and manage geospatial datasets (imagery, raster, vector, point clouds) for AI/ML use. Build and maintain automated preprocessing and postprocessing tools (using open-source libraries like GDAL/PROJ and Python). Partner with AI/ML, Pipeline, Product teams, and leadership to ensure data coverage, quality, and readiness for model development and deployment.

Design, build, and own scalable MLOps infrastructure for large 2D/3D multimodal datasets. Automate CI/CD and continuous training pipelines, implement dataset versioning and storage strategies, and deploy monitoring/observability systems to track model performance and data drift across production ML systems. Collaborate with ML, annotation teams, and TPMs to define requirements.