Senior ML Platform Architect — Remote Impact at Scale

Remote Jobs; United States; Remote; USD 175,000 - 241,000; Full time

Benefits offered by this job

Competitive pay

Equity and benefits

Hybrid/Remote flexibility

Job summary

Faire is seeking a Senior Staff Machine Learning Platform Engineer to own the technical vision of Faire’s ML platform. You will set standards, influence architecture, and lead cross-functional initiatives enabling data science velocity at scale. You will design systems that support many data science teams and production workloads.

You will guide ML workflows, stay ahead with latest AI tools, and mentor engineers while partnering with production teams to drive scalable ML across the organization.

Qualifications

• 10–12 years building large-scale ML or data platforms.
• Graduate degree in CS/Engineering preferred.
• Expertise with Databricks lakehouse and cost optimization.

Responsibilities

• Define long-term architecture of Faire’s ML platform.
• Establish company-wide standards for MLOps and CI/CD.
• Lead adoption of Unity Catalog, multi-workspace strategies.
• Architect scalable ML workflows using Spark, Delta Lake, MLflow.
• Optimize performance, reliability, and cost of the ML platform.
• Evaluate and integrate emerging Databricks features.
• Mentor ML engineers and raise technical bar.

Skills

Databricks lakehouse

Unity Catalog

Workflows

Distributed systems

Cloud architecture

ML infrastructure

Leadership

LLM workflows

Education

Graduate degree in CS/Engineering

Tools

Spark

Kafka

Databricks

Snowflake

Fivetran

Iceberg

Airflow

Kubernetes

Job description

Faire is seeking a Senior Staff Machine Learning Platform Engineer to own the technical vision of Faire’s ML platform. You will set standards, influence architecture, and lead cross-functional initiatives enabling data science velocity at scale. You will design systems that support many data science teams and production workloads.

You will guide ML workflows, stay ahead with latest AI tools, and mentor engineers while partnering with production teams to drive scalable ML across the organization.