• Design, develop and deploy SaaS services end-to-end—from understanding requirements and designing solutions to deployment, production support, scalability and performance • Be on the fore front of AI-native SDLC by using agents throughout design, coding, testing, and documentation • Research new technologies and solutions to improve existing products and explore new directions • Implement comprehensive automated testing at the component level and collaborate with SDET(s) • Participate in standards bodies and shape next-generation standards like W3C, WebAuthn, and OAuth • Work directly with customers - product calls to understand the problems and an on-call rotation of incident escalation

• Lead the development of innovative AI-native cloud systems from concept to beta product • Design and implement cloud-based products and prototypes, with a focus on leveraging GenAI technologies • Drive deep technical collaboration with Engineering, Product Management, and Research teams to translate ambiguous business challenges into high-impact, scalable solutions • Drive the model development process through all stages, from initial research to production-ready implementations • Work closely with engineering teams to ensure smooth integration of solutions into production environments • Stay current with the latest advancements in AI-related cloud technologies • Act as a domain expert and mentor for other engineers, raising the bar for cloud engineering and AI innovation across the organization

• Write, review, and ship production code — this is a hands-on role and will stay that way as the team grows • Design and develop features across the full stack, primarily in TypeScript, alongside Ruby components • Partner with product and design to translate supplier pain points into scalable solutions • Champion a high-velocity delivery culture — small iterations, continuous deployment, regular releases • Drive data-informed decisions: instrument features, interpret results, guide what gets built next • Leverage AI-powered development tools actively — we're aggressive adopters, and we expect our engineers to be too

• Contribute features to vLLM that empower the newest models with the latest NVIDIA GPU hardware features; profile and optimize the inference framework (vLLM) with methods like speculative decoding, data/tensor/expert/pipeline-parallelism, prefill-decode disaggregation. • Develop, optimize, and benchmark GPU kernels (hand-tuned and compiler-generated) using techniques such as fusion, autotuning, and memory/layout optimization; build and extend high-level DSLs and compiler infrastructure to boost kernel developer productivity while approaching peak hardware utilization. • Define and build inference benchmarking methodologies and tools; contribute both new benchmark and NVIDIA’s submissions to the industry-leading MLPerf Inference benchmarking suite. • Architect the scheduling and orchestration of containerized large-scale inference deployments on GPU clusters across clouds. • Conduct and publish original research that pushes the pareto frontier for the field of ML Systems; survey recent publications and find a way to integrate research ideas and prototypes into NVIDIA’s software products.