The roleWe 're hiring a founding computer vision engineer. You 'll work directly with the Head of AI on the perception models behind our products, and take ownership of the datasets and evaluation work that make them perform in the field. This is an applied, hands‐on role. Our hardest problems aren 't about picking the newest architecture, they 're about building the right training data, evaluating honestly enough to know what actually improved, and spending real time in the footage to understand what a model is getting wrong. If you like problems where the answer is in the data rather than the paper, you 'll do well here.What you 'll doTrain, evaluate, and improve vision models on real production videoOwn our training datasets: sourcing and selecting data, defining label schemas, and reviewing annotation qualityDesign evaluations that reflect real deployment conditions, and report results clearly and honestlyInvestigate model failures directly on footage and identify their root causesWork with our annotation team to target the data most likely to improve performanceRun experiments end to end: from question, to training runs, to a result someone can act onRequirementsBachelor 's in computer science, engineering, mathematics, or a related field ( or equivalent practical experience). A Master 's in computer vision, machine learning, or a related area is welcome but not required2+ years of hands‐on applied computer vision experience, ideally in industry or on systems deployed beyond a research environmentY 've trained and deployed a vision model on real‐world data end to end,



including the messy partsStrong Python and PyTorchSolid understanding of evaluation: train/test splits, precision/recall tradeoffs, and why a solid validation score doesn 't always mean a good model in productionComfortable spending significant time reviewing video footage; understanding what the camera actually sees is a real part of this jobWilling to say "I don't know" and "I thinkI got that wrong. " We'd rather hear it earlyProfessional working proficiency in French and EnglishBased in CanadaTechnical environmentRequired:Git and a standard branch/review workflowLinux — comfortable working entirely over SSH on remote GPU machines: bash, background jobs, services, reading logsCloud — hands‐on experience with AWS and/or GCP, including compute, storage, and managing your own training environmentsPyTorch and CUDA — GPU‐based training, checkpoint management, and experiment tracking.OpenCV and ffmpeg — decoding, cropping, and processing video at scaleNumPy, pandas, scikit‐learn, and standard annotation data formatsNice to have:Experience with video and temporal models, not just single imagesObject tracking and multi‐object associationModel export and inference optimizationExperience with annotation platforms and running an annotation workflowExposure to manufacturing, robotics, or industrial inspectionYou 'll be the second person on the AI team, with direct ownership of a core part of the product and a very short path from your work to something running on a live production line.Competitive salary and stock optionsHealth and wellness spending account, from $500 to $1,000 annuallyLatest MacBook and modern sales toolingOpportunity to grow as the company scales

📌 Computer Vision Engineer (Montreal)
🏢 Assembler AI
📍 Montreal