
Digital sports entertainment and gaming company
Senior Machine Learning Engineer, Personalization
At Draft Kings, 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 on the Personalization team, you'll shape how players experience our products by building real-time, one-to-one personalization at scale. You'll design and deploy machine learning systems that deepen engagement, improve retention, and drive long-term player value. Working across Engineering, Product, and Analytics teams, you'll turn data into impactful experiences while advancing how we build, deploy, and optimize ML solutions. This role blends hands-on model development with system design and technical leadership in a fast-moving, high-impact environment.
What You'll Do:
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Lead end-to-end machine learning initiatives focused on improving player engagement and retention, from initial concept through production deployment.
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Build scalable, reusable machine learning pipelines with a focus on reliability, maintainability, and performance.
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Design and manage CI/CD workflows for machine learning using tools like MLflow, Jenkins, and Git Ops to enable automated and efficient model deployment.
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Monitor model performance in production, implementing retraining strategies, drift detection, and continuous optimization.
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Partner with cross-functional teams to translate business goals and user insights into high-impact machine learning solutions.
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Mentor other engineers and help define best practices for machine learning system design, development, and deployment.
What You'll Bring:
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Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical field.
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At least 3 years of experience working with machine learning systems in production environments.
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Strong proficiency in Python and SQL, with experience working on distributed data platforms such as Spark.
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Proven experience delivering production-grade machine learning models that drive measurable business impact.
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Hands-on experience with Databricks for managing machine learning workflows, model lifecycle, and collaborative development.
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Experience designing experiments and analyzing A/B tests to validate and optimize model performance.
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Strong communication and collaboration skills, with experience mentoring or leading technical initiatives.
Join Our Team
We’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.
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DraftKingsについて

DraftKings
PublicDraftKings is a digital sports entertainment and gaming company that operates daily fantasy sports contests and online sportsbook betting. The company offers mobile and web-based platforms for users to participate in fantasy sports and place sports wagers.
1,001-5,000
従業員数
Boston
本社所在地
$12.5B
企業価値
レビュー
10件のレビュー
3.0
10件のレビュー
ワークライフバランス
3.2
報酬
3.8
企業文化
2.8
キャリア
2.5
経営陣
2.1
45%
知人への推奨率
良い点
Good compensation and benefits
Flexible working arrangements
Fun work environment and products
改善点
Poor management and leadership issues
Lack of communication
Limited growth opportunities
給与レンジ
55件のデータ
Junior/L3
Mid/L4
Senior/L5
Director
Junior/L3 · Data Engineer
4件のレポート
$126,100
年収総額
基本給
$97,000
ストック
-
ボーナス
-
$126,100
$126,100
面接レビュー
レビュー5件
難易度
3.2
/ 5
期間
14-28週間
体験
ポジティブ 0%
普通 80%
ネガティブ 20%
面接プロセス
1
Application Review
2
Online Assessment
3
Recruiter Screen
4
Technical Phone Screen
5
Final Interview
6
Offer Decision
よくある質問
Coding/Algorithm
Technical Knowledge
Behavioral/STAR
Past Experience
Culture Fit
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