Software Engineer, AI accelerator Runtime

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role You will build the low-level device runtime that turns compiled programs into efficient, functional and performant execution on OpenAI’s custom AI accelerator. This software will schedule kernel launches, manage device memory and address spaces, coordinate synchronization, and expose reliable abstractions to higher-level runtimes and frameworks. You will work at the boundary of software and hardware, partnering with compiler, kernel, architecture, verification, and silicon teams to define interfaces and validate behavior. You will also use and improve event-based, cycle-accurate simulation to develop runtime capabilities before silicon is available, diagnose performance and correctness issues, and guide hardware-software co-design. In this role, you will: - Design and implement the low-level device runtime for OpenAI custom silicon. - Build kernel-launch scheduling, command submission, queueing, dependency tracking, and completion handling. - Manage device memory spaces, allocation, virtual-to-physical mappings, data movement, and lifetime across concurrent workloads. - Implement synchronization primitives, events, barriers, streams, and ordering guarantees that are correct and efficient. - Define clean interfaces between the runtime, drivers, firmware, compiler-generated code, kernels, and higher-level execution systems. - Use event-based, cycle-accurate simulators to develop, validate, debug, and performance-tune runtime behavior before and after silicon avai

Department: Scaling; Team: Hardware