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.

• 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

Job Summary At Red Hat we believe the future of AI is open and we are on a mission to bring the power of open-source LLMs and vLLM to every enterprise. Red Hat AI Inference team accelerates AI for the enterprise and brings operational simplicity to GenAI deployments. As leading developers, maintainers of the vLLM project, and inventors of state-of-the-art techniques for model quantization and sparsification, our team provides a stable platform for enterprises to build, optimize, and scale LLM deployments. As a Senior Machine Learning Engineer focused on model optimization algorithms, you will work closely with our product and research teams to develop SOTA deep learning software. You will collaborate with our technical and research teams to develop LLM training and deployment pipelines, implement model compression algorithms, and productize deep learning research. If you are someone who wants to contribute to solving challenging technical problems at the forefront of deep learning in the open source way, this is the role for you. Join us in shaping the future of AI! What you will do - Contribute to the design, development, and testing of various inference optimization algorithms in the vLLM , and related projects, such as llm-d. - Create and manage inference serving deployment pipelines - Benchmark, profile, and evaluate different parallelizations, quantization and sparsification approaches to determine the best performance for specific hardware and models - Stay up-to-date with the latest advancements in the open source LLM model architecture, LLM Inference parallelizations/optimizations techniques, and quantization research - Stay up-to-date of latest CPU and GPU hardware architecture and features to boost AI inference performance - Give thoughtful and prompt code reviews - Continuous collaboration with internal and external open source comitters and contributors while contributing to vLLM and related projects ​ What you will bring - Strong understanding of machine learning and deep learning fundamentals with experience in one or more of LLM Inference Optimizations, Computer Vision, NLP, and reinforcement learning - Experience with tensor math libraries such as PyTorch and NumPy - Strong programming skills with proven experience implementing Python based machine learning solutions - Ability to develop and implement research ideas and algorithms - Experience with mathematical software, especially linear algebra - Understanding of Linear Algebra, Gradients, Probability, and Graph Theory - BS, or MS, or PhD in computer science or computer engineering or a related field. #LI-MD2 #AI-HIRING About Red Hat Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40+ countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact. Inclusion at Red Hat Red Hat’s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village. Equal Opportunity Policy (EEO) Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law. Red Hat does not seek or accept unsolicited resumes or CVs from recruitment agencies. We are not responsible for, and will not pay, any fees, commissions, or any other payment related to unsolicited resumes or CVs except as required in a written contract between Red Hat and the recruitment agency or party requesting payment of a fee. 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