Senior Backend Engineer - Ads ML System at Faire

Toronto, Ontario, Canada -

Full Time

Start Date

Immediate

Expiry Date

06 Feb, 26

Salary

214500.0

Posted On

08 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Backend Systems, Kotlin, Java, Machine Learning, Model Training, Distributed Systems, Service Design, Performance Optimization, Collaboration, Data Science, Real-Time Scoring, Auction Mechanisms, Scoring Pipelines, Mentorship, System Architecture

Industry

Software Development

Description

About Faire Faire is an online wholesale marketplace built on the belief that the future is local — independent retailers around the globe are doing more revenue than Walmart and Amazon combined, but individually, they are small compared to these massive entities. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so that small businesses everywhere can compete with these big box and e-commerce giants. By supporting the growth of independent businesses, Faire is driving positive economic impact in local communities, globally. We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours. About this Role: Faire’s Ads teams are building the next generation of marketplace intelligence — powering personalized discovery and efficient ad delivery for our brands and retailers. Our systems connect the right products with the right buyers through intelligent pricing, auction mechanisms, and large-scale retrieval and ranking pipelines. As a Senior Backend Engineer, you’ll take ownership of the core algorithms and infrastructure that shape the way our ads and search systems decide what to show, to whom, and at what price. You’ll partner with Data Science and Infrastructure teams to design training workflows, deploy models, and operate low-latency inference services, owning the end-to-end model lifecycle (feature generation → training → evaluation → deployment → monitoring) for Ads retrieval, ranking, and pricing. This role offers a rare opportunity to shape both product and technical direction in a fast-moving, high-impact area at Faire. What You’ll Do: Own critical services within our Kotlin-based backend, driving reliability, scalability, and low-latency performance across billions of ad and search events. Design and evolve large-scale retrieval, ranking, and pricing systems to optimize relevance, performance, and marketplace efficiency. Develop auction-based delivery systems and scoring pipelines that balance advertiser ROI with retailer experience. Collaborate cross-functionally with Data Science to prototype, evaluate, and productionize machine learning models and their training workflows. Build and operate online inference services (e.g., feature access, real-time scoring, canary/shadow deploys) with clear latency and reliability SLOs. Mentor engineers and contribute to team best practices in design reviews, performance tuning, and system architecture. We’re Excited About You Because You Have: Experience: 5+ years in backend systems, including 2+ years in designing, deploying, and scaling ML inference and online serving systems. Technical Depth: Proficiency in Kotlin, Java, or similar object-oriented languages; comfort with distributed systems, service design, and performance optimization. ML Fluency: Hands-on experience with model training pipelines and operating production online inference/serving, including deploying models to real-time scoring paths on ads/search/ranking. Collaboration: Strong communication skills and a track record of partnering with cross-functional stakeholders, from DS to PM, to deliver complex systems. Mindset: Curiosity and a drive to understand complex market dynamics; passion for applying data and algorithms to real-world business problems. Education: Bachelor’s degree in Computer Science/Engineering or equivalent industry experience. Technologies We Use and Teach: Kotlin, Java, Python Snowflake, Airflow, Spark TensorFlow, PyTorch, AWS Hugging Face Transformers, Pandas, NumPy JUnit, Hibernate, Guice, Jersey HTTP, JSON, Protocol Buffers MySQL, Dynamodb, CockroachDB, Elasticsearch 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. Hybrid Faire employees currently go into the office 2 days per week on Tuesdays and Thursdays. Effective starting in January 2026, employees will be expected to go into the office on a third flex day of their choosing (Monday, Wednesday, or Friday). Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting. Applications for this position will be accepted for a minimum of 30 days from the posting date. Why you’ll love working at Faire We are entrepreneurs: Faire is being built for entrepreneurs, by entrepreneurs. We believe entrepreneurship is a calling and our mission is to empower entrepreneurs to chase their dreams. Every member of our team is taking part in the founding process. We are using technology and data to level the playing field: We are leveraging the power of product innovation and machine learning to connect brands and boutiques from all over the world, building a growing community of more than 350,000 small business owners. We build products our customers love: Everything we do is ultimately in the service of helping our customers grow their business because our goal is to grow the pie - not steal a piece from it. Running a small business is hard work, but using Faire makes it easy. We are curious and resourceful: Inquisitive by default, we explore every possibility, test every assumption, and develop creative solutions to the challenges at hand. We lead with curiosity and data in our decision making, and reason from a first principles mentality. Faire was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have headquarters in San Francisco and Kitchener-Waterloo, and a global employee presence across offices in Toronto, London, and New York. To learn more about Faire and our customers, you can read more on our blog. Faire provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity or gender expression. Faire is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Accommodations are available throughout the recruitment process and applicants with a disability may request to be accommodated throughout the recruitment process. We will work with all applicants to accommodate their individual accessibility needs. To request reasonable accommodation, please fill out our Accommodation Request Form (https://bit.ly/faire-form) Privacy For information about the type of personal data Faire collects from applicants, as well as your choices regarding the data collected about you, please visit Faire’s Privacy Notice (https://www.faire.com/privacy)

Responsibilities

As a Senior Backend Engineer, you will own critical services within the Kotlin-based backend, ensuring reliability and low-latency performance. You will also design and evolve large-scale retrieval, ranking, and pricing systems to optimize marketplace efficiency.