Senior Machine Learning Engineer

DraftKings•Boston, MA

About The Position

As a Senior Machine Learning Engineer at DraftKings, you will shape the technical direction of the Machine Learning Platform, which supports data science, analytics, and machine learning across the company. You will lead the architecture and evolution of infrastructure spanning real-time data pipelines, data governance, and scalable machine learning services. Working with Engineering and Data Science teams, you will establish technical standards, lead complex initiatives, and build reliable, efficient systems. The role also includes mentoring engineers, advancing automation, and influencing how machine learning infrastructure is built and operated at scale.

Requirements

At least 8 years of experience in software engineering, data engineering, or machine learning infrastructure, including experience leading complex technical initiatives.
Deep expertise in distributed data systems and streaming technologies, including Kafka, Spark or comparable frameworks, schema management, and open table formats such as Apache Iceberg or Delta Lake.
Strong experience with Databricks or similar lakehouse platforms, including data governance, access management, and scalable data architecture.
Proficiency with Amazon Web Services (AWS), Terraform, infrastructure as code, cloud networking, and identity management.
Proven ability to define technical architecture, establish engineering standards, and influence decisions across multiple engineering and data teams.
Experience managing production infrastructure, including monitoring, on-call support, incident response, and operational reliability.
Experience with AI-assisted development tools and ability to apply emerging technologies to improve engineering efficiency and automate workflows.
Strong communication, collaboration, and mentorship skills, including the ability to translate complex technical concepts into actionable plans and guide engineers at different experience levels.

Nice To Haves

Experience with Snowflake.
Experience with machine learning serving infrastructure.
Experience with cloud cost optimization.
Experience with AI-powered developer tooling.

Responsibilities

Define the technical architecture and roadmap for the Machine Learning Platform, including real-time data ingestion, lakehouse governance, and machine learning serving infrastructure.
Design and scale distributed data systems using technologies such as Kafka, Spark Structured Streaming, and Databricks, emphasizing reliability, automation, and self-service capabilities.
Establish engineering standards and architectural best practices and drive their adoption across teams to improve platform consistency, scalability, and performance.
Lead complex engineering initiatives by defining technical specifications, establishing milestones, and guiding implementation through design and code reviews.
Partner with Data Engineering, Business Intelligence, Data Science, and Architecture teams to advance data sharing, platform connectivity, and enterprise data infrastructure.
Own the reliability and operational performance of critical platform services, including monitoring, incident response, and continuous improvement.
Drive cloud cost efficiency through resource optimization, cost monitoring, and scalable compute management frameworks.
Champion AI-assisted engineering practices, develop intelligent automation tools, and mentor engineers to strengthen technical expertise and expand platform ownership.