Senior Staff Machine Learning Platform Engineer @ Faire

About this role

<div class="content-intro"><p><span style="font-weight: 400;"><strong>About Faire</strong></span></p> <p>Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. 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 businesses can grow and local communities can thrive.</p> <p>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.</p></div><p><strong>About this role</strong></p> <p>As the Senior Staff Machine Learning Platform Engineer, you will own the technical vision and evolution of Faire’s ML platform. You will set standards, influence org-wide architecture, and lead complex, cross-functional initiatives that unlock data science velocity at scale. &nbsp;This role will also be key to adapting ML workflows to take advantage of modern AI productivity tools. You won’t just build models, you will architect the systems that allow those models to help tens of thousands of small retailers compete and grow their local businesses.</p> <p><strong>What You Will Do</strong></p> <ul> <li>Define and drive the long-term architecture of Faire’s ML platform including training, inference, feature management, governance</li> <li>Establish company-wide standards for code quality, testing, MLOps (CI/CD), experimentation, model lifecycle management, and observability</li> <li>Lead adoption and advanced use of Unity Catalog, multi-workspace strategies, and data/ML mesh patterns</li> <li>Architect highly scalable ML workflows using Spark, Delta Lake, and MLflow</li> <li>Optimize performance, reliability, and cost of the ML platform</li> <li>Evaluate and integrate emerging Databricks features</li> <li>Stay ahead of the curve by engaging with the latest developments in machine learning and AI</li> <li>Serve as senior ML technical advisor to Faire’s data science and production engineering teams&nbsp;</li> <li>Represent Faire at ML conferences and meetups</li> <li>Mentor ML engineers and raise the overall bar for Machine Learning at Faire</li> </ul> <p><strong>What it takes</strong></p> <ul> <li>10-12 years of experience building and improving large-scale ML or data platforms.</li> <li>A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field.</li> <li>Deep expertise in Databricks lakehouse architecture, including governance via Unity catalog, orchestration via Workflows, and cost optimization</li> <li>Proven ability to design systems that support multiple data science teams and production workloads</li> <li>Strong background in distributed systems, ML infrastructure, and cloud architecture.</li> <li>Demonstrated technical leadership across teams and orgs; ability to influence without authority</li> <li>Experience integrating LLM workflows into enterprise platforms is a plus</li> <li>Previous contributions to open source ML Infrastructure projects or research publications is a very strong plus</li> </ul> <p><strong>Tech Stack</strong></p> <p>Faire uses a modern cloud based tech stack.&nbsp; For this role, you’ll want to be proficient with the following:</p> <table style="border-collapse: collapse; width: 93.0556%; height: 324.75px; border-width: 1px;" border="1"> <tbody> <tr style="height: 47.5938px;"> <td style="width: 17.8161%; height: 47.5938px; border-width: 1px;"> <p style="text-align: center;"><strong>Category</strong></p> </td> <td style="width: 82.1839%; height: 47.5938px; border-width: 1px;"> <p style="text-align: center;"><strong>Technologies</strong></p> </td> </tr> <tr style="height: 47.5938px;"> <td style="width: 17.8161%; height: 47.5938px; border-width: 1px;"> <p><strong>Languages</strong></p> </td> <td style="width: 82.1839%; height: 47.5938px; border-width: 1px;"> <p>Python, SQL, Kotlin</p> </td> </tr> <tr style="height: 47.5938px;"> <td style="width: 17.8161%; height: 47.5938px; border-width: 1px;"> <p><strong>ML Frameworks</strong></p> </td> <td style="width: 82.1839%; height: 47.5938px; border-width: 1px;"> <p>PyTorch, PySpark, MLFlow&nbsp;</p> </td> </tr> <tr style="height: 67.1875px;"> <td style="width: 17.8161%; height: 67.1875px; border-width: 1px;"> <p><strong>Big Data &amp; Processing</strong></p> </td> <td style="width: 82.1839%; height: 67.1875px; border-width: 1px;"> <p>Spark, Kafka, Databricks, Snowflake, Fivetran, Iceberg, Unity Catalog, Datadog, Airflow, Cockroach DB, MySQL</p> </td> </tr> <tr style="height: 67.1875px;"> <td style="width: 17.8161%; height: 67.1875px; border-width: 1px;"> <p><strong>Cloud &amp; Infrastructure</strong></p> </td> <td style="width: 82.1839%; height: 67.1875px; border-width: 1px;"> <p>AWS, S3, SageMaker, Kubernetes, Docker, GitHub Actions, Terraform</p> </td> </tr> <tr style="height: 47.5938px;"> <td style="width: 17.8161%; height: 47.5938px; border-width: 1px;"> <p><strong>Generative AI</strong></p> </td> <td style="width: 82.1839%; height: 47.5938px; border-width: 1px;"> <p>Claude Sonnet 4.5, ChatGPT 5.2</p> </td> </tr> </tbody> </table> <p><strong>Salary Range</strong></p> <p>Canada: the pay range for this role is $248,000 to $341,000 per year.&nbsp;</p> <p>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.</p> <p>Faire uses Artificial Intelligence (AI) to screen and select applicants