Senior Product Engineer - Listing Quality

Title: Senior Product Engineer - Listing Quality Location: Kitchener-Waterloo, ON; Toronto, ON

Why Listing Quality at Faire?

Our mission in the Brand pillar is to make it easy for Brands to create high-quality product listings on Faire, keep them up to date, and get them in front of retailers. Having access to well-curated catalogs of products lets Main Street retailers compete with big-box stores, and we’re responsible for the first step in that journey.

As a Senior Product Engineer on the Listing Quality team, you’ll make every product on Faire look and read its best. We use computer vision and LLMs to improve hero images, crops and product descriptions across millions of listings, and we prove the impact through online experiments.

About this role:

You’ll partner closely with Brand Data Science. They own model quality. You own getting model outputs into the product reliably, at catalog scale, and measurably. When it’s faster to test an idea yourself, you do: tune the prompt, build the eval, and run the experiment.

This is an ideal role for engineers who want to work where machine learning meets production systems, and who care as much about whether retailers click as whether the job succeeded. You like turning a data scientist’s prototype into something that runs every day without anyone thinking about it, and you’re just as happy building the first prototype yourself.

You’re entrepreneurial and data-driven, with a strong product sense, and you’re excited about learning how our customers’ businesses work and what they need in order to succeed. You balance your excitement for building features with a focus on their long-term maintainability. You want to use technology to help your local independent retailers and brands succeed, and to experiment with the best AI productivity tools on the market to accelerate customer impact.

What you’ll do:

• Build and own the backend services and data paths that take model-generated enhancements from batch pipelines into the live product
• Design the infrastructure that makes catalog-scale generation dependable: backfills, freshness and staleness detection, coverage monitoring, and versioning of generated content
• Shorten the loop from data science prototype to production experiment through clean handoff contracts, output auditing tools and evaluation hooks
• Ship changes behind experiment flags, and own test plans, launch readiness and post-launch health
• Make cost, latency and quality tradeoffs when running LLM and vision workloads over large datasets
• Mentor fellow engineers on both technical and product judgement

Qualifications:

You may be a great fit if you have:

• 5+ years of experience
• Proficiency in at least one of Java, Kotlin, or JavaScript
• Comfort with agentic coding tools
• Familiarity with HTML, CSS, TypeScript, and frameworks/libraries like React and Next.js
• Experience building or operating data pipelines (Airflow or similar, a warehouse like Snowflake)
• Comfort working with ML or LLM outputs in production. You don’t need to train models, but you understand how they fail
• Fluency with experimentation, including designing a rollout and telling a real result from noise
• A bachelor’s degree in Computer Science/Software Engineering or equivalent industry experience
• A love for technology, and an insatiable curiosity for new tools to solve real problems

Don’t meet every qualification? We know great candidates come from many different backgrounds and experiences. If you’re excited about the role, we’d encourage you to apply.

Technologies we use & teach:

Experience with similar technologies is welcome - you don’t need experience with our exact stack.

• Frontend: React, NextJS, JavaScript
• Backend: Kotlin, Java, Guice, Jersey, Hibernate, JUnit
• Datastores: MySQL, CockroachDB
• Infrastructure & Cloud: AWS
• APIs & Data: HTTP, JSON, Protocol Buffers

Salary range:

Canada: the pay range for this role is $156,000 - $214,500 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.