Machine Learning Engineer, Richmond

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 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 who can help build AI/ML systems for provenance, ownership verification, item intelligence, fraud detection, search, and marketplace trust.

This is a practical engineering role. You will work close to product and backend engineering, turning ambiguous marketplace and verification problems into production systems.

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, build, and deploy ML systems that support collectible verification, provenance signals, item matching, search, recommendations, trust, and fraud detection

Work with structured and unstructured data, including item metadata, images, text descriptions, transaction signals, and marketplace activity

Build evaluation pipelines for model quality, confidence scoring, false positives, false negatives, and human review workflows

Prototype and productionize computer vision, LLM, embedding, classification, ranking, or anomaly detection systems where appropriate

Collaborate with backend engineers working in Node.js and TypeScript to integrate ML services into the product

Create reliable APIs, data pipelines, monitoring, and feedback loops for ML-powered product features

Help define what should be automated, what should stay human-reviewed, and how confidence should be represented to users

Document assumptions, model limitations, risks, and tradeoffs clearly

What We’re Looking For

Experience building ML or AI systems that solve real product or operational problems

Strong Python skills and familiarity with ML libraries such as PyTorch, TensorFlow, scikit-learn, pandas, OpenCV, or similar

Ability to evaluate model performance beyond simple accuracy, including edge cases, error analysis, and production failure modes

Experience working with APIs, cloud services, databases, queues, or backend systems

Comfort operating in an early-stage startup where requirements are evolving and ownership is high

Clear communication, pragmatic technical judgment, and the ability to explain tradeoffs to technical and non-technical teammates

Nice to Have

Experience with computer vision, image similarity, embeddings, vector databases, LLMs, RAG, or multimodal models

Experience with marketplace, fraud, trust and safety, collectibles, authentication, logistics, fintech, or high-value asset platforms

Familiarity with MLOps tools such as MLflow, Weights&Biases, Airflow, or similar

Experience with TypeScript, Node.js, React, or Next.js

Interest in trading cards, sports cards, TCGs, comics, or collector communities

How to Apply Please apply with your resume and links to relevant projects, technical writing, GitHub, portfolio work, or examples of shipped ML systems.

Do not include passwords, API keys, government ID numbers, banking details, SIN, copies of permits, or other sensitive credentials in your application. Any legally required onboarding documents will only be requested after the appropriate hiring stage through secure channels.

Orgn Technologies Inc. is an equal opportunity employer. We evaluate candidates based on relevant skills, experience, judgment, and ability to contribute to the role.

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