Senior Applied AI/ML Scientist - Search

Faire•Toronto, ON

14h•CA$168,000 - CA$231,000•Hybrid

About The Position

As a Senior Applied AI/ML Scientist on the Search group, you’ll help shape the technical vision, machine-learning algorithm strategy, and system design behind one of our most important growth levers: Search (think about what you do when you land on any e-commerce site). You’ll advance real-time Search and Recommendation systems that power next-generation shopping experiences. You’ll work at the frontier of algorithms, combining large language models, natural-language processing, query understanding, deep learning, transformer-based sequential modeling, graph neural networks, and structured behavioral data to return hyper-relevant, personalized products and brands for every user query. This is a rare chance to influence end-to-end personalization in a high-scale, deeply multi-modal environment while collaborating closely with a talented team of scientists and engineers.

Requirements

4+ years of experience building large-scale ML systems, including 2+ years in search, recommendation, or ads ranking.
Hands-on experience with deep-learning libraries (e.g. PyTorch) and vector-search infrastructure (e.g. Faiss, ScaNN, Pinecone).
A strong record of productionizing models that blend LLMs (e.g. BERT, GPT-class) with structured features to drive personalization.
A product-focused mindset and a bias toward execution—you move quickly from paper to prototype to production.
Strong Python skills, deep respect for system reliability and ownership, and experience operating in high-stakes environments.
Excellent communication and cross-functional influence that raise the technical bar beyond your immediate team.

Nice To Haves

Contributions to open-source ML libraries or peer-reviewed publications in ML/AI.
Master’s or PhD in Computer Science, Statistics, or a related STEM field.

Responsibilities

Contribute to our next-generation Search engine by integrating LLMs, query understanding, dense-vector retrieval, deep personalization embeddings, multi-stage ranking, and reinforcement learning to serve personalized product feeds with sub-100 ms latency.
Design and productionize natural-language search and discovery systems so that intelligent agents can generate relevant and personalized collections, explain search results, and assist retailers with browsing, filtering, and evaluation.
Lead model development and GPU-based deployment efforts, leveraging frameworks like Triton to scale inference reliably and efficiently.
Share best practices around model development, agent-workflow evaluation, and MLOps, and help teammates level up through code reviews and technical guidance.