• Own the Visual Gateway: Deliver the features that identify the first thing millions of users see when they open our apps. • Drive Systematic Quality: Improve the reliability and velocity of our experimentation systems, ensuring our visual tests are statistically sound and performant. • Scale Discovery: Build the components that allow us to personalize the 'look and feel' of the platform, not just the content list. • Feature & Component Ownership: Design and implement specific solutions for Multi-Armed Bandit (MAB) systems and visual feature pipelines. • Self-governing Delivery: Own the end-to-end implementation of defined tasks, from data ingestion to production deployment, with moderate autonomy. • System Optimization: Proactively identify and fix bottlenecks in team systems to improve quality, reliability, or engineering velocity. • Collaborative Quality: Participate actively in design and code reviews, providing constructive feedback and ensuring high technical standards within your scope. • Data-Driven Execution: Set up and monitor online experiments (A/B tests and bandit rollouts) to measure the impact of presentation features on user interaction.

• Unterstützung in der Erhebung einer neuartigen Datenbasis und algorithmische Nutzung dieser Daten • Entwicklung eines neuartigen Algorithmus im Kontext des maschinellen Lernens • Umsetzung eines konzeptuellen Datenmodells zur Repräsentation semantischer Zusammenhänge und Förderung interoperabler Strukturen.

How You’ll Make an Impact As an Applied Machine Learning Engineer, you will collaborate with architects, data scientists, agentic AI developers, platform engineers, and the product team to build advanced AI and ML capabilities into our platform. Your work will drive innovation in generative AI and beyond, integrating and customizing a wide range of machine learning techniques to solve complex problems in healthcare. By developing next-generation AI agents, algorithms, and computation engines, you will help Strata strengthen its market leadership, improve operational efficiency, and support healthcare providers in delivering high-quality care while maintaining financial health. A Day in the Life - Read and translate the latest research (e.g., arXiv papers) into production-ready solutions in Python. - Prototype and iterate on machine learning models, focusing on areas such as regression, causal inference, optimization, and vector embeddings. - Collaborate with cross-functional teams to embed ML and AI capabilities directly into our software platform. - Partner with data scientists to design experiments and apply statistical concepts to real-world data. - Optimize, test, and scale ML models to support mission-critical healthcare analytics. Our Technology Stack Our core platform is used by more than half of the nation’s leading healthcare providers, enabling them to leverage financial, operational, and clinical data. Our AI and ML stack includes: - Languages & Libraries: Python, PyTorch, NumPy, Pandas, Polars, PyMC - Infrastructure: AWS, Snowflake, Docker, GitHub - Techniques & Tools: - Regression (with and without Bayesian priors) - Vector embeddings, similarity, clustering - Core statistics and distributions for EDA - Optimization methods (multi-armed bandit, mixed integer programming) - Causal inference and probabilistic modeling What We’re Looking For We’re seeking a technically curious engineer who thrives on turning theory into practice. The ideal candidate has: - Strong experience implementing ML models in Python. - Familiarity with regression, embeddings, causal inference, and optimization techniques. - Experience applying statistical methods to exploratory data analysis. - Comfort working with modern ML libraries and frameworks. Bonus points if you have worked with: - NLP tasks (LLMs, spaCy, neural networks). - Recommender systems, latent factors, matrix factorization. - Graph algorithms. - Claude Code, Docker, and GitHub.ess computation engine. Estimated Salary Range: $117,000-150,000 Actual salary will be determined based on factors including, but not limited to, skill set and level of experience. This salary range is a good faith estimate of base pay. Strata also provides discretionary variable pay programs based on role. In addition, Strata provides a comprehensive benefits package including retirement benefits, health and welfare benefits, paid time off, parental leave, life and accident insurance, and other voluntary and well-being benefits. Find out more about Strata benefits here. How we work: The preferred location for this role is in Chicago, IL or St. Louis, MO. We value our people spending time together and have campuses hosting in-person events located in both cities. We are truly a hybrid environment with all team members experiencing the flexibility to work from home. Thinking about applying? Research shows that women and underrepresented groups tend to apply to jobs only when they check every box on a job posting. If you’re currently reading this and hesitating to click “Apply” for that reason, we encourage you to go for it! A true passion and excitement for making an impact is just as important as work experience. Should you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please reach out to careers@stratadecision.com. Here @ Strata… Our culture is driven by our people solving problems together. We embrace learning, collaboration, and continuous career growth. Together, we lift our customers, our products, our company, and our community. We believe that each of our team member’s unique perspectives and experiences is what drives innovation and positive change. Our individual differences are what make us a more forward-thinking organization. We foster a culture of inclusion, equity and belonging, regardless of race, religion, disability, sex, sexual orientation, gender identity or national origin. Our Core Values: While we celebrate what makes each member of our team unique, our core values are what connect us. They set clear expectations for how we approach our work and how each of us can positively influence the experience of our team and our customers. - We connect with positive intent. - We are helpful. - We own it. - We get better every day. - We are humble.

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 Marketing Intelligence team, you will build the systems and infrastructure that power machine learning across DraftKings' Casino, Sportsbook, and Fantasy verticals. Rather than focusing on a single model or use case, you will enable entire portfolios of models by designing scalable, reliable platforms for feature engineering, model deployment, and production workflows. You'll work closely with Data Science, Engineering, and Product teams to reduce duplication, accelerate time-to-production, and raise the overall quality of machine learning systems. This role blends distributed data engineering, ML system design, and technical leadership in a high-impact, fast-paced environment. What you'll do as a Senior Machine Learning Engineer - Lead the design and development of scalable ML infrastructure and data pipelines that support multiple Data Science teams across Marketing Intelligence - Build reusable frameworks and workflows for feature engineering, model training, deployment, and backfilling at scale - Design and maintain CI/CD systems for data and ML pipelines, enabling reliable and automated production deployments - Improve system reliability through monitoring, alerting, and observability across data and ML workflows - Partner with Data Scientists to productionize models, standardize workflows, and eliminate redundant engineering effort - Optimize large-scale data processing workflows (e.g., Spark) for performance, cost, and reliability - Drive improvements in developer experience through better abstractions, tooling, and shared patterns - Mentor engineers and contribute to raising the team's standards for system design, code quality, and operational excellence What you'll bring - Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical field - Strong Python and SQL skills, with hands-on experience in distributed data processing (e.g., Spark) - Experience designing and maintaining data/ML pipelines and orchestration workflows in production environments - Experience with CI/CD for data or ML systems, including testing, deployment, and release management - Experience working with cloud-based data platforms such as Databricks, Snowflake, or AWS (open to equivalent experience) - Strong understanding of system design tradeoffs, including scalability, reliability, and maintainability - Proven ability to deliver end-to-end solutions and operate them in production with a high sense of ownership - Strong communication and collaboration skills, with experience working across Data Science and Engineering teams #LI-AN1 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.