Lead the architecture and evolution of DraftKings’ machine learning platform, including real-time data pipelines, lakehouse governance, distributed data systems, and scalable model-serving infrastructure. Establish engineering standards, guide complex initiatives, improve reliability and cloud cost efficiency, and partner across engineering and data teams. The role also includes mentoring engineers, managing production operations, and developing AI-assisted automation tools.
At DraftKings, AI is becoming an integral part of both our present and future, powering how work gets done today, guiding smarter decisions, and sparking bold ideas. It’s transforming how we enhance customer experiences, streamline operations, and unlock new possibilities. Our teams are energized by innovation and readily embrace emerging technology. We’re not waiting for the future to arrive. We’re shaping it, one bold step at a time. To those who see AI as a driver of progress, come build the future together.
The Crown Is YoursAs a Senior Machine Learning Engineer on our ML Platform Team, you’ll build the cloud infrastructure and systems that power machine learning across DraftKings. You’ll design scalable platforms, automate deployments, and strengthen the reliability of the tools that help our teams bring machine learning models into production. Working alongside Data Scientists and Machine Learning Engineers, you’ll solve complex engineering challenges using Python, Terraform, and modern cloud technologies. Your work will shape how we scale machine learning capabilities across the organization while creating opportunities to expand your expertise in production machine learning systems.
What you’ll do
- Design, build, and maintain scalable cloud infrastructure that supports machine learning workloads, including compute, storage, networking, and access management using Terraform.
- Develop continuous integration and continuous deployment (CI/CD) pipelines, automation, and self-service tools that streamline infrastructure provisioning and application deployments.
- Own the design, deployment, and ongoing performance of production systems, driving improvements in reliability, monitoring, and cost efficiency.
- Build and optimize data infrastructure to support high-volume batch and streaming workloads, including real-time data ingestion using Kafka and lakehouse technologies.
- Strengthen platform security and governance through access controls, infrastructure policies, and resource management across shared cloud environments.
- Collaborate with Data Scientists, Machine Learning Engineers, and Infrastructure Teams to develop reusable solutions that support model training, deployment, and monitoring.
- Lead technical design discussions, review code, and mentor engineers while advancing engineering standards and best practices across the team.
- Evaluate emerging cloud and infrastructure technologies to identify opportunities to improve platform scalability, performance, and operational efficiency.
What you’ll bring
- A Bachelor’s Degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- At least 3 years of professional software engineering experience focused on cloud infrastructure, platform engineering, site reliability engineering, or data platforms.
- Strong proficiency in Python, including experience developing, testing, and maintaining production-level applications and automation tools.
- Hands-on experience with infrastructure as code tools such as Terraform or Pulumi, including developing reusable modules and managing cloud resources.
- Experience designing and supporting distributed systems in cloud environments, preferably Amazon Web Services (AWS), with knowledge of networking, identity and access management, and security best practices.
- Experience with CI/CD pipelines, workflow orchestration, and containerization technologies such as GitHub Actions, Jenkins, Argo, Airflow, or Docker; Kubernetes experience is a plus.
- Familiarity with distributed data processing technologies such as Databricks, Spark, or Kafka, along with experience supporting batch or streaming data workloads.
- A strong sense of ownership, experience supporting production systems, and the ability to lead complex technical initiatives while communicating effectively across teams. Prior machine learning experience is a plus, but not required.
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Join Our TeamWe’re a publicly traded (NASDAQ: DKNG) technology company headquartered in Boston. As a regulated gaming company, you may be required to obtain a gaming license issued by the appropriate state agency as a condition of employment. Don’t worry, we’ll guide you through the process if this is relevant to your role.
The US base salary range for this full-time position is 134,400.00 USD - 168,000.00 USD, plus bonus, equity, and benefits as applicable. Our ranges are determined by role, level, and location. The compensation information displayed on each job posting reflects the range for new hire pay rates for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific pay range and how that was determined during the hiring process. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.Jackpocket New York, New York, USA Office
100 Park Avenue, New York, NY, United States, 10017
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