Software Engineer, GPT Infrastructure

About the Team The GPT Infrastructure team builds systems that turn advances in model inference and optimization into reliable production capabilities. We enable OpenAI workloads to be qualified and optimized across new accelerator platforms without requiring a one-off port and tuning effort for every hardware target. Our work spans distributed systems, model execution, compilers and runtimes, performance engineering, secure partner integrations, evaluation systems, and developer tooling. We build the infrastructure that makes optimization workflows automated, reproducible, and trustworthy. About the Role We are seeking a software engineer to help build the platform that qualifies and optimizes inference workloads across heterogeneous compute environments. You will develop both OpenAI-hosted services and secure partner-side software for running long-lived optimization workflows. These workflows generate candidate kernels, runtime configurations, and serving-stack changes; compile and execute them on target hardware; verify their correctness; measure their performance; and use the results to guide further optimization. You will work across model architecture, distributed execution, compilers, runtimes, networking, and accelerator systems. A central part of the role is turning research prototypes and one-off hardware bring-up efforts into reliable, reusable infrastructure with clear contracts, reproducible results, strong observability, and well-defined security boundaries. Key Responsibilities - Design, build, and operate APIs and control-plane services for long-running workload qualification and optimization campaigns, including scheduling, retries, checkpointing, resource budgets, and observability. - Build secure partner-side execution and evaluation software that can compile, run, verify, profile, and benchmark candidate artifacts on accelerator hardware. - Integrate model workloads, hardware profiles, compiler toolchains, runtimes, serving engines, and distributed-execution backends into a repeatable platform. - Develop correctness and performance evaluation systems spanning output fidelity, latency, throughput, memory footprint, accelerator utilization, communication efficiency, scaling behavior, and cost efficiency. - Automate the generation, evaluation, and improvement of kernels, runtime configurations, parallelization strategies, and serving-stack changes. - Diagnose performance and correctness issues across model code, kernels, compilers, runtimes, memory systems, networking, collective communication, and hardware. - Build artifact-management, provenance, regression-testing, and qualification workflows for kernels, binaries, configurations, evaluation results, and deployment reports. - Turn experimental research workflows into reliable product surfaces with clear interfaces, actionable failure modes, and strong developer ergonomics. - Collaborate with Research, Inference Engineering, Runtime and Compiler teams, Infrastructure, Security, Product, and Strategic Partnerships to onboard and optimize new compute platforms. - Drive technical architecture and execution across ambiguous initiatives spanning OpenAI systems and partner environments. Qualifications - Strong software engineering experience building distributed systems, infrastructure platforms, production services, developer platforms, or orchestration systems. - Proficiency in one or more systems-oriented languages such as Python, C++, Go, or Rust. - Experience designing and operating APIs, job orchestration systems, durable workflows, or large-scale backend services. - Strong understanding of Linux, networking, storage, containers, distributed execution, and modern infrastructure architectures. - Ability to reason about model execution and diagnose problems across software and hardware boundaries. - Experience using profiling, tracing, benchmarking, and measurement to guide engineering decisions. - Strong ownership and the ability to work effectively across research, engineering, security, product, and external-partner teams. Preferred Skills - Experience with AI infrastructure, model inference, distributed ML systems, or inference-serving platforms. - Familiarity with GPU or accelerator architecture, memory hierarchies, interconnects, collective communication, and distributed model execution. - Experience with compilers, runtimes, kernels, or performance engineering using technologies such as CUDA, ROCm, Triton, LLVM, or MLIR. - Familiarity with inference engines or serving systems such as vLLM, SGLang, Triton Inference Server, or comparable internal systems. - Experience with model partitioning, sharding, tensor or expert parallelism, and compute–communication tradeoffs. - Experience building remote-execution systems, secure partner-facing infrastructure, evaluation harnesses, or artifact pipelines. - Experience with automated optimization, search systems, coding agents, or evaluator-driven systems that iterativel