Staff Machine Learning Engineer

Boulevard

2h•$118,400 - $171,000•Remote

About The Position

Boulevard is seeking a Staff ML Engineer to help shape the next generation of their data and AI platform. This role involves designing, building, and scaling machine learning and AI-powered solutions on top of their modern data platform to power conversational analytics, automation, and customer-facing experiences. The engineer will collaborate closely with product, engineering, and cross-functional partners to tackle complex data challenges and identify high-impact AI opportunities for customers.

Requirements

7+ years of combined experience in Data Engineering and Machine Learning roles
3+ years of experience building, deploying, and operating machine learning systems in production environments.
Strong programming skills in Python and SQL, with experience using modern ML frameworks and AI tooling.
Experience designing scalable data pipelines, data quality, governance and working with modern cloud data platforms such as Snowflake, Databricks, or similar.
Strong understanding of semantic layer design, MLOps practices, and data modeling principles that support reliable CI/CD for ML workloads.
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

Design, build and deploy production-grade machine learning and AI systems that power customer-facing analytics, automation, and intelligent product experience.
Develop and operationalize end-to-end ML workflows - from data preparation and feature engineering to model training, deployment, monitoring and continue improvement.
Build scalable AI solutions leveraging LLMs, embeddings, recommendation systems, forecasting models, and other modern ML techniques.
Collaborate with Product and Engineering teams to identify high-impact use cases and deliver seamless AI-powered data products.
Design and evolve semantic data models that enable accurate, consistent, and scalable analytics across the organization.
Mentor engineers, provide technical leadership, and influence architecture and engineering standards across teams.