• 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.

• Develop open source software to serve inference of trained AI models running on GPUs • Contribute to the development of disaggregated serving for Dynamo-supported inference engines (vLLM, SGLang, TRT-LLM) • Innovate in the management and transfer of large KV caches across heterogeneous memory and storage hierarchies • Build new features to the Dynamo Rust Runtime Core Library and design, implement, and optimize distributed inference components in Rust and Python • Balance a variety of objectives: build robust, scalable, high performance software components to support our distributed inference workloads

• At H1, we believe access to the best healthcare information is a basic human right • Our mission is to provide a platform that can optimally inform every doctor interaction globally • This promotes health equity and builds needed trust in healthcare systems • To accomplish this our teams harness the power of data and AI-technology to unlock groundbreaking medical insights and convert those insights into action that result in optimal patient outcomes and accelerates an equitable and inclusive drug development lifecycle