Machine Learning Engineer - Co-op

*This is a paid co-op opportunity. We are focusing on hiring an undergraduate co-op student in Canada, who is eligible for a co-op via their university. We are focusing on students graduating soon and interested in a full time career with us in the long run.

Job description

Retailogists is looking for a Machine Learning Engineer - Co-op to join our engineering team and work primarily on Avia – our edge-based retail analytics platform.

Avia (https://www.retailogists.com/avia) is a plug-and-play device you install in your store that connects Shopify POS sales data with in-store traffic and demographics, delivering eCommerce-level analytics for physical retail. It captures how many people visit, when they convert, and what drives revenue per visitor; all visualized in one dashboard. With Avia, you go beyond POS data to understand non-transactional behavior.

At the heart of Avia is the device's core software running complex image processing and classification on edge, and producing anonymized demographic insights. This device will be your primary area of focus – among other occasional side quests.

What you’ll do

• Work on the core image processing and machine learning software behind Avia

• Build and improve models using a mix of modern GenAI-based image processing and traditional computer vision techniques

• Design and optimize ML systems that run directly on edge devices under compute, memory, and privacy constraints

• Deploy and adapt software to the hardware environment used by Avia devices, with hands-on exposure to Linux-based systems

• Contribute to cloud-connected ML workflows and deployments in GCP

• Support the development of predictive ML features for Avia and contribute to other Retailogists projects when needed

• Explore interesting related areas in federated ML, synthetic data, and edge processing on IoT systems

What we’re looking for

 • Excellent communication skills and a strong work ethic with a professional, team-oriented attitude

• Hands-on experience with machine learning, computer vision, and image processing through internships, co-ops, hackathons, research, or open-source work

• Familiarity with both modern GenAI approaches and traditional computer vision techniques

• Familiarity/interest with Internet-of-Things and embedded systems

• Understanding of the practical challenges of deploying models in resource-constrained environments such as edge devices

• Comfort working across ML and software engineering tasks, including experimentation, implementation, and deployment

• Exposure to Linux and an interest in working close to hardware for deployment and troubleshooting

• Familiarity with at least one major cloud platform such as GCP, AWS, or Azure

Nice to have

• Experience with edge ML, on-device inference, model optimization, or privacy-preserving ML systems

• Exposure to embedded systems, IoT architecture, or micro-computer based technology

• Retail tech, eCommerce, or Shopify-related experience

• Interest in transitioning into a full-time role in the near future

Working setup

• Remote anywhere in Canada, or Hybrid (offices in Montreal, Toronto, and Kitchener)

• Must be legally authorized to undertake a co-op placement in Canada

About Retailogists

Retailogists is your comprehensive retail partner dedicated to optimizing your ROI. Leveraging cutting-edge technology and data-centric digital marketing, we fuel your business growth and help you achieve your objectives. Our seasoned experts simplify the complexities of unified commerce, from devising effective digital strategies to crafting high-performance transactional platforms and executing intelligent marketing plans.

We excel in streamlining your business operations through automation and synchronization, covering areas such as transactional solutions for both B2C and B2B, merchandising, logistics optimization, and systems integrations, including ERP, inventory, and CRM.

We also specialize in building state-of-the-art data infrastructure that empowers you with actionable business insights, ranging from straightforward reporting to AI-driven decision-making.