Senior Machine Learning Engineer

DraftKings•Boston, NY

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 join a team of algorithm experts and data science technologists building innovative data products that solve analytically complex challenges across the business. You’ll turn cutting-edge machine learning research into scalable, production-ready systems that power critical decisions and customer experiences. Working at the intersection of data, engineering, and experimentation, you’ll shape high-impact solutions from concept to deployment while influencing best practices across the organization.

Requirements

At least 3 years of software engineering experience designing, building, and operating machine learning and artificial intelligence systems in production environments.
A strong foundation in machine learning and statistics, with proven experience applying them to solve complex, real-world problems.
Hands-on experience managing the full machine learning lifecycle, from data preparation and feature engineering to deployment and monitoring.
Expertise in Python, along with experience in additional languages or tools that support scalable machine learning systems.
Experience working with modern platform and infrastructure technologies such as Databricks, Kubernetes, and Terraform.
Strong communication and leadership skills, with the ability to influence stakeholders and collaborate effectively across teams.
A passion for mentoring teammates, sharing knowledge, and fostering technical growth within the team.
The ability to work autonomously, proactively identifying opportunities and driving technical improvements from idea to execution.

Responsibilities

Design and refine scalable pipelines for training, evaluating, and deploying machine learning models across diverse use cases.
Build rigorous testing and statistical validation frameworks to measure model performance and ensure reliability in production.
Optimize models for production readiness, improving computational efficiency, latency, and resource utilization.
Implement real-time monitoring systems to detect data drift, model decay, and performance degradation, proactively maintaining model accuracy.
Partner with cross-functional teammates to design, build, and deploy end-to-end machine learning applications that scale with business growth.
Champion machine learning best practices, including responsible and ethical AI development, documentation, and reproducibility.
Identify and lead technical initiatives that elevate platform capabilities and advance the team’s machine learning standards.