Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team As a platform company powering businesses all over the world, Stripe processes payments, runs marketplaces, detects fraud, helps entrepreneurs start an internet business from anywhere in the world. Stripe's AppSec Engineers build scanning platforms and tooling, alert and remediation pipelines, ensure reliable data, and transform data from various inputs and applications used to ultimately represent security posture across all of Stripe. At Stripe, we are building security scanning and posture infrastructure using data science tooling and big data systems that will help us with scale while making onboarding and analysis of new data easy and transparent. Rather than traditional commercial tooling, you’ll help to drive codified processes, data analytics and automation. This is a unique challenge for a cyber professional interested in non-traditional security monitoring and response designed to function within a development operations framework. You’ll maintain strong partnerships with the security assessment and security discovery teams on capabilities and other security teams to understand the interfaces to those systems useful for monitoring and response throughout Stripe. What you’ll do Responsibilities - Understand data tooling available at Stripe and determine how to best leverage, modify, or fork them for use by security - Create libraries, tooling and platform needed to operationalize continuous security testing tools at scale - Enable holistic data integration to support advanced data analytics - Maintain libraries that enable interaction with various internal and external data sources and systems used for correlation of security posture logic - Create a reliability layer for metrics related to the data pipeline both for easy debugging and constant improvement of bottlenecks - Create APIs to help security teams access underlying data Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements - A strong engineering background with interest in data. - Experience writing production Python and Go code - Experience developing and maintaining distributed systems built with open source tools - Experience building libraries and tooling that provide beautiful abstractions to users - Experience integrating with CI/CD developer flows - Experience with tools such as Kafka, Airflow and various Notebook technology - 4+ years of relevant experience in Security - Experience as a consumer of data science tooling and infrastructure - Experience security technologies including endpoint detection, network technologies, AWS cloud services - Strong understanding of the technical capabilities needed for an effective appsec and vulnerability management capability - Ability to build strong relationships and drive cross functional projects with engineering partners Preferred qualifications - Ability to drive concurrent projects and initiatives while managing operational responsibilities - An exemplary, user-focused communication style; emphasizing clarity, empathy and accuracy - Demonstrated success working remotely - Ability to deliver capabilities to teams in an iterative manner while building towards a larger vision - Demonstrated success overseeing internal tool development and automation at scale - Experience with collection of compliance artifacts, security incidents and risk awareness

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 Lead Infrastructure Engineer, you'll design, build, and operate the machine learning platform and Databricks infrastructure that powers scalable, reliable data science at DraftKings. You'll own the backbone that makes model development, training, and deployment fast, repeatable, and cost-aware-so teams can move from ideas to impact without friction. Working alongside Data Science, Machine Learning, Data Engineering, and Infrastructure partners, you'll turn evolving use cases into durable platform capabilities. You'll lead projects end to end, strengthening reliability, automation, and developer experience across the stack. What you'll do as a Lead Infrastructure Engineer - Own and operate Databricks infrastructure with a focus on reliability, scalability, performance, and cost optimization. - Build and manage cloud infrastructure on Amazon Web Services using infrastructure-as-code tools like Terraform. - Author and review technical designs that enable scalable, automated, and reproducible machine learning workflows - Lead and mentor engineers, helping grow team capability and strengthen day-to-day execution. - Partner closely with data scientists, data engineers, machine learning engineers, and Infrastructure teams to align platform capabilities to real-world needs. - Drive engineering initiatives from technical planning through delivery, production rollout, and long-term maintenance. - Stay current on data platform and machine learning platform trends, applying best practices that improve platform efficiency, usability, and governance. - Coach and mentor teammates, raising the bar through strong technical feedback, thoughtful enablement, and shared ownership. What you'll bring - At least 5 years of experience in machine learning platform, data platform, data engineering, or infrastructure engineering roles. - Hands-on experience administering and operating Databricks in production environments. - Deep familiarity with infrastructure as code, including Terraform or Pulumi, and proven ability to manage change safely at scale. - Experience with AWS, Docker, Kubernetes, and continuous integration and continuous delivery pipelines. - People management experience is a plus - Strong Python skills and familiarity with machine learning tooling such as MLflow, pandas, and scikit-learn. - A track record of owning complex systems end to end, including reliability improvements, incident follow-up, and performance tuning. - Clear, confident communication skills, including strong technical documentation and the ability to align cross-functional partners. #LI-SP1 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.

Deeter Analytics At Deeter Analytics, we’re building something that doesn’t get built twice in a generation. Our goal is to create a fundamental trading model as capable as today’s most advanced AI systems — but applied to global markets. Not incremental signals or isolated strategies, but a system that can continuously interpret, learn from, and act on the evolving state of the world. We train on large-scale, real-time social data — capturing how narratives form, how sentiment propagates, and how collective behavior drives markets. This requires operating at the frontier of data infrastructure, model design, and compute, all tightly integrated into a single system. You’ll work alongside a small group of elite engineers, AI researchers, and traders, in an environment defined by speed and ownership. We run experiments continuously. Ideas move from concept to production in hours. And the feedback loop is immediate — measured directly in live performance. About the role You will build and optimize the systems that turn data and compute into model capability. This role sits at the intersection of distributed systems, GPU infrastructure, and model training — ensuring that both our in-house models and state-of-the-art external models can be trained efficiently at scale. We prefer systems that maximize learning per unit of compute, not just systems that run. What you’ll work on ● Designing and operating distributed training systems on GPU infrastructure ● Optimizing GPU utilization, throughput, and training efficiency ● Translating model requirements into efficient system configurations ● Improving training speed, cost efficiency, and reliability ● Debugging failures in high-cost, high-pressure training environments What we’re looking for We’re looking for people who understand how systems behave under real constraints — and know how to push them to perform. Strong signals: ● You have run or significantly contributed to large-scale training workloads or compute-intensive systems ● You have a strong understanding of distributed systems in practice, including public cloud environments like AWS ● You understand how infrastructure behaves beneath the abstraction: ○ networking constraints ○ GPU/CPU utilization ○ memory and I/O bottlenecks ○ hardware limits at scale ● You can reason about how systems can be tuned for more efficient training and resource usage ● You have debugged systems where failures were non-trivial and costly ● You move quickly, identify bottlenecks, and eliminate them without being asked Bonus signals: ● Experience optimizing systems where small efficiency gains had large downstream impact ● Experience working under strict compute or cost constraints ● Experience debugging distributed or asynchronous systems with non-obvious failure modes ● You use AI tools to accelerate debugging, development, and iteration ● You care about building systems that are measurably efficient, not just functional

• Own the design, deployment, and operation of OpenStack and Kubernetes environments — ensuring platform performance, scalability, and resilience for GPU workloads • Build and improve infrastructure using infrastructure-as-code and GitOps practices, driving automation across provisioning, deployment, and operational workflows • Optimise GPU workload scheduling using Kubernetes and NVIDIA tooling, and implement monitoring, logging, and alerting to ensure platform stability • Lead incident response and drive continuous improvement of reliability across the platform • Maintain strong security controls across infrastructure and container layers — RBAC, network policies, and tenant isolation • Work closely with Platform, DevOps, AI, Product, and Support teams to align infrastructure capabilities with customer and platform requirements