8+ years in software engineering, data engineering, or machine learning infrastructure with leadership experience.
Expertise in distributed data systems and streaming technologies like Kafka and Spark.
Strong background with Databricks or similar lakehouse platforms, focusing on data governance and architecture.
Proficient in AWS, Terraform, and infrastructure as code practices.
Ability to define technical architecture and influence engineering standards across teams.
Experience managing production infrastructure and ensuring operational reliability.
Familiarity with AI-assisted development tools and emerging technologies.
Responsibilities
Define the technical architecture and roadmap for the Machine Learning Platform.
Design and scale distributed data systems with a focus on reliability and automation.
Establish engineering standards and best practices to enhance platform performance.
Lead complex engineering initiatives with clear technical specifications and milestones.
Collaborate with cross-functional teams to improve data sharing and platform connectivity.
Ensure operational performance of platform services through monitoring and incident response.
Drive cloud cost efficiency through resource optimization and scalable management frameworks.
Mentor engineers and champion AI-assisted engineering practices.
Benefits
Opportunity to shape the technical direction of a leading machine learning platform.
Collaborative work environment with cross-functional teams.
Mentorship opportunities to advance technical expertise.
Focus on innovation with AI-assisted engineering practices.
Engagement in complex, impactful engineering initiatives.
Full Job Description
The Crown Is Yours
As a Senior Machine Learning Engineer, you’ll shape the technical direction of the Machine Learning Platform that powers data science, analytics, and machine learning capabilities across DraftKings. You’ll lead the architecture and evolution of critical infrastructure, from real-time data pipelines and data governance to scalable machine learning services. Working across Engineering and Data Science teams, you’ll establish technical standards, drive complex initiatives, and build reliable, efficient systems that support our growing business. Your impact will extend beyond the platform as you mentor engineers, advance automation, and influence how we build and operate machine learning infrastructure at scale.
What you’ll do as a Senior Machine Learning Engineer
Define the technical architecture and roadmap for our 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, with a focus on reliability, automation, and self-service capabilities.
Establish engineering standards and architectural best practices, driving 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.
What you’ll bring
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, and 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, with the ability to translate complex technical concepts into actionable plans and guide engineers at different experience levels. Experience with Snowflake, machine learning serving infrastructure, cloud cost optimization, or AI-powered developer tooling is a plus.
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About DraftKings
DraftKings is an American daily fantasy sports contest and sports betting operator. The company allows users to enter daily and weekly fantasy sports–related contests and win money based on individual player performances in five major American sports, Premier League and UEFA Champions League soccer, NASCAR auto racing, Canadian Football League, the XFL, mixed martial arts and Tennis.
In August 2018, DraftKings launched DraftKings Sportsbook in New Jersey becoming the first legal mobile sports betting operator in the state. Since launching in New Jersey, DraftKings has opened mobile sports betting operations in Indiana, Pennsylvania, West Virginia and opened in New Hampshire December 30, 2019 after reaching contract with the New Hampshire Lottery.
Retail sports betting is available in Iowa, Mississippi and New York. DraftKings Sportsbook mobile and retail sports betting products allow bettors in each state engage in betting for most major U.S. and international sports.
As of April 2016, the majority of U.S. states consider fantasy sports a game of skill and not gambling. In November 2016, FanDuel and DraftKings, the two largest companies in the daily fantasy sports industry, reached an agreement to merge. However the merger was terminated in July 2017 due to it being blocked by the Federal Trade Commission as the combined company would have controlled a 90 percent of the market for daily fantasy sports. As of July 2017, DraftKings had eight million users.
