Senior Machine Learning Engineer

DraftKings Inc.•Boston, MA

23h•$134,400 - $168,000

About The Position

As a Senior Machine Learning Engineer at DraftKings, you will shape the technical direction of the Machine Learning Platform supporting data science, analytics, and machine learning across the company. You will lead the architecture and evolution of infrastructure such as real-time data pipelines, data governance, and scalable machine learning services. Working across Engineering and Data Science, you will establish technical standards, lead complex initiatives, and build reliable, efficient systems. You will also mentor engineers, advance automation, and influence how machine learning infrastructure is built and operated at scale. The role focuses on defining the platform architecture and roadmap; designing and scaling distributed data systems; establishing engineering standards and best practices; guiding complex initiatives from specifications through implementation; and partnering across data and architecture teams. You will own the reliability and operational performance of critical services, drive cloud cost efficiency, and champion AI-assisted engineering and intelligent automation.

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 the 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.
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 and develop intelligent automation tools.
Mentor engineers to strengthen technical expertise and expand platform ownership.