• Lead the investigation and resolution of complex infrastructure, networking, and platform-related incidents. • Act as a senior escalation point for operational teams during critical service-impacting events. • Support large-scale NVIDIA GPU infrastructure and high-performance networking environments. • Troubleshoot complex Linux, Kubernetes, networking, storage, and hardware-related issues. • Analyze platform performance, capacity, stability, and reliability trends to proactively identify risks. • Lead root cause analysis activities and drive long-term corrective actions. • Collaborate with engineering teams, hardware vendors, and datacenter personnel to resolve complex technical challenges. • Participate in major incident management and service restoration activities. • Provide technical leadership for Kubernetes platform operations and supporting infrastructure services. • Drive improvements in platform reliability, observability, monitoring, and operational processes. • Identify opportunities to automate repetitive operational activities and improve operational efficiency. • Support the adoption and operation of AI-powered infrastructure services and operational capabilities through k0rdent AI. • Mentor and support AI Infrastructure & Platform Operations Engineers.

Role Description Build and maintain scalable AI infrastructure, enabling teams to run ML experiments, deploy machine learning models, and implement MLOps pipelines for production-grade AI. - Design distributed training pipelines for large-scale machine learning and deep learning models. - Optimize compute and storage resources for cloud-based AI/ML workloads on AWS, GCP, or Azure. - Collaborate with data scientists and ML engineers to deploy models in production efficiently. - Implement monitoring, logging, and alerting for model performance and AI workflows. - Ensure scalable, maintainable, and reliable AI infrastructure to support real-time and batch ML applications. Qualifications - 5+ years in Python and ML infrastructure. - Experience in cloud AI platforms (AWS Sagemaker, GCP AI Platform, Azure ML). - Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD for ML. - Experience with distributed systems, data pipelines, and high-performance computing for AI. - Hands-on with deep learning frameworks like TensorFlow or PyTorch.

Role Description We are looking for a Senior Infrastructure Engineer who can help us build a high-reliability, high-throughput, high-performance, globally-distributed advertising platform. Our customers will rely on our platform to run thousands of advertising auctions per second, all day every day, in order to show ads to their customers. This industry has evolved to the point where reliable performance is table-stakes — we won’t get a pass just because we’re a new startup. You’ll work extremely closely with the rest of our engineering team to make sure that the entire product is designed to operate at global scale. We’ll rely on you to design and implement the fundamental infrastructure necessary for our success. Your key responsibilities will be: - Infrastructure Engineering: Helping design our data pipelines, our network architecture, our deployment strategies, and any other aspect of our product that involves core infrastructure. We’ll rely on you to be the expert in the room as well as a hands-on engineer capable of doing the work yourself (but not alone!). - Technical Leadership: Lead by example to help develop our engineering team’s practices, particularly when it comes to testing and scaling a product whose core functionality involves large-scale, complex, sensitive, data processing. - Incident Response: Alongside the rest of us, you’ll be on-call for the systems we build. You’ll play a large part in helping us minimize our operational burden through process design and shift-left mentality, but at the end of the day we all have to keep the code running. Qualifications - Infrastructure Platform Experience: You’ve built platforms, tools, and capabilities that other engineers can rely upon to deliver a best-in-class product. - DevOps/SRE Experience: You’ve been on-call before and have a good sense for how teams can minimize operational burden. - Hands-on expertise: You may have managed or led at points in your career, but you still code regularly and are interested in continuing to do so. - Strong written communication skills: You are used to writing about, speaking about, and generally communicating complex technical subject matter both to other engineers and to non-engineers. - Early-stage mentality: You understand that success at a startup involves grit and determination. - AI forward: You are actively experimenting with or using AI as part of your software engineering practice. - High ownership: You care a lot about your work and when you ship a product, you make sure it continues to solve problems for the customer. Requirements - Adtech experience: You’ve worked in adtech, particularly mobile adtech, and have a good understanding of the broader ecosystem and market. - Stack experience: We’re running in AWS, we use terraform to manage infra, we use EKS for kubernetes clusters, we deploy with ArgoCD, we merge code with Github, we write a lot of Golang, we put application state in Postgres, we store a lot of event data in Clickhouse, we use Datadog for observability. Benefits - The annual US base salary range for this role, and other engineering roles, is $150,000 – $300,000. - We offer equity compensation and top-tier medical, dental, and vision benefits. - We have a generous hardware budget for a computer, monitor, and other core equipment necessary to work effectively on a remote team. - We care about the quality of your work more than the specific hours you spend getting it done, and try to minimize the number of synchronous meetings in favor of greater flexibility. - There is no in-office requirement.

• Develop and maintain Rust-based features across credit, investments, and regulatory compliance domains • Write SQL queries in PostgreSQL • Contribute to unit and integration tests, helping maintain code quality and coverage • Work closely with the team on code reviews, architecture discussions, and technical documentation • Investigate and fix bugs in critical production flows