Senior Machine Learning Engineer

This role shapes the technical direction of the Machine Learning Platform that powers data science, analytics, and machine learning capabilities across the organisation. It leads 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, the role establishes technical standards, drives complex initiatives, and builds reliable, efficient systems that support a growing business.

• 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, 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 optimisation, 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.

• 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.
• Ability to obtain a gaming licence issued by the appropriate state agency if required for the role.

• Experience with Snowflake, machine learning serving infrastructure, cloud cost optimisation, or AI-powered developer tooling.

• Full-time position.
• US-based role; compensation range covers all US locations.

• Bonus, equity, and benefits as applicable.
• DraftKings will guide employees through the gaming licence process if relevant.

DraftKings is a publicly traded (NASDAQ: DKNG) technology company headquartered in Boston. It is a regulated gaming company. AI is becoming an integral part of DraftKings' present and future, powering how work gets done, guiding smarter decisions, and transforming customer experiences and operations.