Senior Staff Machine Learning Platform Engineer

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. 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 engineers compete and grow their local businesses.

* Define and drive the long-term architecture of Faire’s ML platform including training, inference, feature management, governance * Establish company-wide standards for code quality, testing, MLOps (CI/CD), experimentation, model lifecycle management, and observability * Lead adoption and advanced use of Unity Catalog, multi-workspace strategies, and data/ML mesh patterns * Architect highly scalable ML workflows using Spark, Delta Lake, and MLflow * Optimize performance, reliability, and cost of the ML platform * Evaluate and integrate emerging Databricks features * Stay ahead of the curve by engaging with the latest developments in machine learning and AI * Serve as senior ML technical advisor to Faire’s data science and production engineering teams * Represent Faire at ML conferences and meetups * Mentor ML engineers and raise the overall bar for Machine Learning at Faire

* 10-12 years of experience building and improving large-scale ML or data platforms. * A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field. * Deep expertise in Databricks lakehouse architecture, including governance via Unity catalog, orchestration via Workflows, and cost optimization * Proven ability to design systems that support multiple data science teams and production workloads * Strong background in distributed systems, ML infrastructure, and cloud architecture. * Demonstrated technical leadership across teams and orgs; ability to influence without authority * Experience integrating LLM workflows into enterprise platforms is a plus * Previous contributions to open source ML Infrastructure projects or research publications is a very strong plus