Senior Machine Learning Engineer

DraftKings Inc.•Boston, MA

8d•$134,400 - $168,000

About The Position

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 partner closely with data scientists, engineers, and product managers to build systems that are well-structured, observable, and scalable. The Fintech Data Science team builds and maintains systems that protect DraftKings and its customers from fraud, payment risk, and abuse. Our mission is to enable explainable and reliable decision-making across all financial and operational flows — from registration and deposits, to gameplay and withdrawals.

Requirements

3+ years of experience writing production Python applications and working with structured data (SQL and data modeling).
Strong software engineering fundamentals — object-oriented design, testing, and version control.
Familiarity with ML Ops frameworks such as model registry, orchestrations, and feature stores.
Experience building or maintaining data and feature pipelines in a data-heavy environment.
Understanding of the ML lifecycle, including feature engineering, training, and model serving.
Ability to debug production issues across data, model, and infrastructure layers.
Strong collaboration and communication skills; able to translate technical solutions into operational outcomes.
Bachelor’s degree in Computer Science, Machine Learning, or a related technical field (Master’s preferred).

Responsibilities

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.
Establish observability and monitoring for data quality, model drift, and service reliability.
Participate in code reviews, design discussions, and incident response, helping to maintain strong engineering practices.
Mentor junior engineers and contribute to shared tooling, documentation, and process improvements across the DS organization.
Support on-call rotations and post-incident reviews to ensure continuous system improvement.