Systems Generalist, GPT Infrastructure

Key Responsibilities

• Design, build, and operate durable APIs and control-plane services for multi-hour or multi-day optimization campaigns, including scheduling, retries, budgets, checkpoints, artifact lineage, and observability.
• Build secure partner-side runner and grader software that can compile, execute, verify, and benchmark candidate artifacts on third-party accelerator hardware.
• Integrate hardware profiles, ISA and toolchain context, compilers, runtimes, and inference-serving engines into a repeatable optimization workflow.
• Turn research prototypes into reliable product surfaces with clear contracts, debuggable failure modes, reproducible outputs, and excellent developer ergonomics.
• Develop correctness and performance evaluation systems spanning latency, throughput, memory use, utilization, and cost efficiency.
• Build artifact, provenance, and qualification workflows that make optimized kernels, binaries, configurations, and reports safe to review and deploy.
• Collaborate with Research, Inference Engineering, Infrastructure, Security, Product, and Strategic Partnerships to deliver production-ready solutions.
• Drive technical architecture and execution across ambiguous, cross-functional initiatives that connect OpenAI systems with partner environments.

Basic Qualifications

• 8+ years of professional software engineering experience building large-scale distributed systems, infrastructure platforms, or cloud services, or equivalent depth of experience.
• Strong programming skills in one or more of C++, Python, Go, or Rust.
• Experience designing and operating highly available backend systems, APIs, job orchestration systems, or durable workflows for production workloads.
• Strong understanding of distributed systems, Linux, networking, storage, containers, and modern cloud architectures.
• Experience debugging complex systems and using measurement, profiling, and benchmarks to guide engineering decisions.
• Proven ability to lead complex technical initiatives as a senior individual contributor and work effectively across organizational boundaries.

Preferred Skills

• Experience with AI infrastructure, inference-serving systems, or large-scale machine learning systems.
• Experience with compilers, runtimes, kernel optimization, or performance engineering; familiarity with technologies such as LLVM, MLIR, Triton, CUDA, or ROCm is a plus.
• Familiarity with GPUs, accelerators, hardware architecture, ISA concepts, or vendor toolchains.
• Experience with inference-serving frameworks or engines such as vLLM, SGLang, Triton Inference Server, or similar systems.
• Experience building developer platforms, external APIs, remote execution systems, or secure partner-facing infrastructure.
• Experience working with strategic cloud, hardware, or infrastructure partners.