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 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 - Lead end-to-end machine learning initiatives focused on improving player engagement and retention, from initial concept through production deployment. - Build scalable, reusable machine learning pipelines with a focus on reliability, maintainability, and performance. - Design and manage CI/CD workflows for machine learning using tools like MLflow, Jenkins, and GitOps to enable automated and efficient model deployment. - Monitor model performance in production, implementing retraining strategies, drift detection, and continuous optimization. - Partner with cross-functional teams to translate business goals and user insights into high-impact machine learning solutions. - Mentor other engineers and help define best practices for machine learning system design, development, and deployment. What You'll Bring - Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical field. - At least 3 years of experience working with machine learning systems in production environments. - Strong proficiency in Python and SQL, with experience working on distributed data platforms such as Spark. - Proven experience delivering production-grade machine learning models that drive measurable business impact. - Hands-on experience with Databricks for managing machine learning workflows, model lifecycle, and collaborative development. - Experience designing experiments and analyzing A/B tests to validate and optimize model performance. - 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.

• Lead end-to-end machine learning initiatives focused on improving player engagement and retention, from initial concept through production deployment. • Build scalable, reusable machine learning pipelines with a focus on reliability, maintainability, and performance. • Design and manage CI/CD workflows for machine learning using tools like MLflow, Jenkins, and GitOps to enable automated and efficient model deployment. • Monitor model performance in production, implementing retraining strategies, drift detection, and continuous optimization. • Partner with cross-functional teams to translate business goals and user insights into high-impact machine learning solutions. • Mentor other engineers and help define best practices for machine learning system design, development, and deployment.

• Develop and fine-tune large language models (LLMs) for intelligent, safe, and responsive browser interactions. • Apply retrieval-augmented generation (RAG), summarization, classification, and intent modeling techniques in real-world browser workflows. • Use modern ML tooling (e.g., Hugging Face, LangChain, Weights & Biases, Ray, or similar) to support end-to-end model development, including experimentation, evaluation, and rollout. • Collaborate closely with product and engineering partners to design, iterate, and launch features that align with user needs and Mozilla’s values. • Design and implement experiments to test model behavior in production, analyze results, and iterate based on findings. • Document and share your work, participating in code reviews and team design discussions.

• Grow out and lead a team of Deep Learning engineers • Steward the end-to-end planning, execution, and delivery of a scoped machine learning applied science agenda • Act as the primary point of contact with infrastructure and data pipeline teams • Exhibit strong judgement in derisking core hypotheses and updating research direction informed by ongoing advances in the state of the art