Staff Data & Machine Learning Engineer

Boulevard

4h•$118,400 - $171,000•Remote

About The Position

Boulevard is seeking a Senior ML Engineer to contribute to the evolution of their data platform and customer-facing data products. This role involves designing, building, and scaling systems for data processing and machine learning/generative AI workloads. The engineer will collaborate with product, engineering, and cross-functional teams to address data challenges, develop ML use cases, and create reliable data solutions for customers.

Requirements

5+ years of experience in data engineering.
2+ years of experience building, deploying, or supporting machine learning systems in production environments.
Strong proficiency in SQL and Python, with experience leveraging ML frameworks and AI tooling.
3+ years of experience working with the modern data stack, including dbt, Snowflake, data streaming technologies, and Looker or Omni.
Strong understanding of semantic layer design, dimensional modeling, and data architecture best practices.
Broad knowledge of data governance, data quality, observability, and analytics/security best practices.
Experience building data products that leverage LLMs, embeddings, forecasting, recommendation systems, or other ML technologies.
Excellent problem-solving, communication, and collaboration skills, with the ability to work effectively across global and cross-functional teams.

Nice To Haves

Experience with Snowflake Cortex is a plus.

Responsibilities

Extend, optimize, and maintain core data models that support customer-facing reports, machine learning, and generative AI workloads.
Build and operationalize ML and AI workflows — from development to deployment and monitoring — that streamline operations, reduce manual effort, and improve customer and business outcomes.
Partner with engineering, product, and analytics teams to deliver seamless integrations and customer-facing data products.
Implement data quality, observability, and governance frameworks to ensure reliable, well-managed data at scale.
Document data flows, integration contracts, and operational runbooks to support efficient scaling and handoff.