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Faire is a B2B wholesale marketplace connecting independent retailers globally with suppliers. This role owns the technical vision and evolution of Faire's ML platform, setting company-wide standards and leading cross-functional initiatives to unlock data science velocity at scale.
You will define and drive the long-term architecture of Faire's ML platform, including training, inference, feature management, and governance. Key responsibilities include establishing company-wide standards for code quality, testing, MLOps, experimentation, model lifecycle management, and observability. You'll lead adoption of Unity Catalog, multi-workspace strategies, and data/ML mesh patterns while architecting highly scalable ML workflows using Spark, Delta Lake, and MLflow.
The role requires optimizing performance, reliability, and cost of the ML platform, evaluating and integrating emerging Databricks features, and staying current with latest ML and AI developments. You'll serve as a senior ML technical advisor to data science and production engineering teams, mentor ML engineers, and represent Faire at ML conferences and meetups.
Required qualifications include 10-12 years building and improving large-scale ML or data platforms, preferably with a graduate degree in Computer Science, Engineering, Statistics, or related field. Deep expertise in Databricks lakehouse architecture (governance via Unity Catalog, orchestration via Workflows, cost optimization) is essential. You need proven ability to design systems supporting multiple data science teams and production workloads, strong background in distributed systems and cloud architecture, and demonstrated technical leadership with ability to influence without authority. Experience integrating LLM workflows and contributions to open-source ML infrastructure projects are strong pluses.
Tech stack includes Python, SQL, Kotlin, PyTorch, PySpark, MLflow, Spark, Kafka, Databricks, Snowflake, Fivetran, Iceberg, Unity Catalog, Datadog, Airflow, and cloud infrastructure platforms.
