DraftKings

Senior Machine Learning Engineer

Boston, Massachusetts, USA · Senior · Full-time

Sponsorship not specified$134k-$134kDetected 27 days ago

PythonCode ReviewSQLMachine LearningData EngineeringData ScienceMLOpsIncident ResponseCommunicationCollaboration

About the role

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 Yours As a Senior Machine Learning Engineer, you'll design, implement, and scale production-grade data and machine learning pipelines that drive measurable business outcomes. You'll

Design, build, and maintain ETL and feature engineering pipelines that power risk and payment applications.
Develop production ML systems, from training and evaluation through deployment and monitoring.
Partner with data scientists to productionize models that integrate into DK's decisioning systems.
Build reliable, well-tested data workflows - from SQL-based feature transformations through model training and deployment.
Participate in code reviews, design discussions, and incident response, helping to maintain strong engineering practices.
Support on-call rotations and post-incident reviews to ensure continuous system improvement.
Strong software engineering fundamentals - object-oriented design, testing, and version control.
Experience building or maintaining data and feature pipelines in a data-heavy environment.

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.

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.
Establish observability and monitoring for data quality, model drift, and service reliability.
Mentor junior engineers and contribute to shared tooling, documentation, and process improvements across the DS organization.
What you'll bring 3+ years of experience writing production Python applications and working with structured data (SQL and data modeling).
Familiarity with ML Ops frameworks such as model registry, orchestrations, and feature stores.

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.
Your recruiter can share more about the specific pay range and how that was determined during the hiring process.

Bachelor's degree in Computer Science, Machine Learning, or a related technical field (Master's preferred).
Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

DKNG) technology company headquartered in Boston.
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.
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.

This listing is sourced directly from DraftKings's careers page and normalized into a canonical job model.