At MapsPeople, we are entering a pivotal phase of AI‑led transformation and growth, shaping the future of spatial intelligence and indoor navigation for large enterprise customers worldwide. To support this evolution, we are looking for a Machine Learning Engineer to take ownership of the systems that power our indoor mapping platform.The Role You will own the full lifecycle of our machine learning systems from data ingestion and model training to deployment, optimization, and production monitoring. This is a hands‑on role at the intersection ofcomputer vision, geospatial data, and real‑world engineering , where model performance directly impacts how people navigate complex indoor environments.You will work on challenging problems such as generalizing models across diverse architectural inputs, transforming raw predictions into usable spatial data, and optimizing inference at scale. As part of a lean, high‑impact team, you will have the opportunity to shape the direction of our ML pipeline, explore next‑generation approaches, and deliver solutions that are directly reflected in the product experience.We imagine you come with5-8 years of experience buildingmachine learning systems in productionStrong proficiency inPython , with clean, maintainable, and well‑tested codeExperience withdeep learning frameworks(PyTorch preferred)Strong background incomputer vision(object detection, segmentation, or classification)Understanding ofML systems and infrastructure, training pipelines, dataset management, and model versioningExperience working withcloud platforms(GCP, AWS, or Azure)Familiarity withcontainers, orchestration, and production deploymentExperience withend‑to‑end production pipelinesincluding data ingestion, preprocessing, model training, inference, and monitoring.Strong understanding ofmodel evaluation and performance metricsAbility to think beyond “model accuracy” and evaluate real‑world impactYou should also have:Experience with geospatial data (coordinate systems, geometry processing, spatial databases)Familiarity with architectural drawings or structured building data (CAD, BIM, etc.)Exposure to foundation models or vision‑language modelsExperience optimizing models for inference (quantization, runtime optimization, hardware acceleration)Background in adjacent domains such as robotics, autonomous systems, medical imaging, satellite imagery, or document AIEngagement with the ML community through open‑source contributions, competitions, or researchA degree in Computer Science, Machine Learning, Mathematics, Physics, Engineering, or a related field is one pathBut strong candidates also come from bootcamps, self‑directed learning, adjacent disciplines, or unconventional career pathsWhat you getProblems Worth Solving:Your work directly impacts how people navigate real buildings, from hospitals to airports. The feedback loop is tangible and immediate.Technical Ownership:You will own critical parts of the ML pipeline in a lean team where your decisions shape architecture and direction.Room to Explore:We are actively adopting modern ML approaches — you won’t be maintaining legacy systems.Indoor mapping sits at the intersection of computer vision, geospatial engineering, and product thinking — a niche with real depth and complexity.Work in an international environment with teams across multiple regions and strong Danish roots.Flexible Work Setup:This role is based in the Toronto / GTA area with a hybrid work model.Competitive Compensation:We offer compensation and benefits aligned with the Toronto market and your experience level.#J-18808-Ljbffr