Loading, please wait...

machine learning engineer

Posted on May 26, 2026 by Employer details ORGN Technologies

Job details

*Machine Learning Engineer* *Orgn Technologies is building infrastructure for the physical collectibles market, starting with high-value trading cards, sports cards, comics, and related collector assets. Our goal is to create a trusted platform where collectors can discover, verify, manage, and safely trade physical collectibles online.* We are an early-stage, funded, and building toward our MVP. The engineering team is small, hands-on, and focused on shipping reliable systems from first principles. We are looking for a Machine Learning Engineer with strong experience in computer vision, vision-language models, and multimodal AI. This role will focus mainly on building the vision pipeline that helps our platform understand collectible images, identify item attributes, compare visual similarity, detect quality issues, and support trust and verification workflows. This is a practical engineering role. You will work closely with product and engineering to turn ambiguous visual recognition and marketplace trust problems into useful ML-powered product features. Location: Richmond, BC Work Model: On-site/hybrid. The initial phase is expected to be mostly in office for fast team building and product execution, with hybrid flexibility as the team scales. Employment Type: Full-time, permanent Pay: CA$95,000.00-CA$135,000.00 per year + Stock Options *What You?ll Do* * Design, prototype, and improve computer vision and vision-language model systems for collectible item understanding * Build ML workflows for image quality checks, item recognition, visual matching, attribute extraction, and anomaly detection * Work with image and text data, including collectible photos, item titles, descriptions, metadata, and human review feedback * Use VLMs and multimodal models to support visual reasoning, classification, similarity, and item intelligence features * Create evaluation pipelines for model quality, confidence scoring, edge cases, false positives, false negatives, and human review workflows * Help define which parts of the vision pipeline should be automated, assisted by AI, or reviewed by humans * Collaborate with product and engineering teammates to integrate model outputs into user-facing marketplace features * Document assumptions, model limitations, risks, and technical tradeoffs clearly *What We?re Looking For* * Experience building ML or AI systems that solve real product, research, or operational problems * Strong experience with computer vision, vision-language models, or multimodal machine learning * Hands-on experience with image modelling tasks such as classification, detection, segmentation, OCR, image similarity, embeddings, or visual retrieval * Strong Python skills and familiarity with ML libraries such as PyTorch, TensorFlow, scikit-learn, OpenCV, pandas, or similar * Ability to evaluate model performance beyond simple accuracy, including edge cases, error analysis, confidence thresholds, and production failure modes * Comfort working with messy real-world visual data, imperfect labels, and evolving product requirements * Clear communication, pragmatic technical judgment, and the ability to explain model tradeoffs to technical and non-technical teammates * Comfort operating in an early-stage startup where ownership is high and requirements are still evolving *Nice to Have* * Experience with VLMs, CLIP-style models, multimodal embeddings, visual question answering, image-text retrieval, or LLM-assisted vision workflows * Experience building image pipelines for marketplaces, ecommerce, authentication, trust and safety, fraud detection, logistics, fintech, or high-value asset platforms * Familiarity with model evaluation tooling, annotation workflows, active learning, or human-in-the-loop review systems * Experience with MLOps tools such as MLflow, Weights & Biases, Airflow, or similar * Interest in trading cards, sports cards, TCGs, comics, or collector communities *How to Apply* Please apply with your resume a