We are seeking a Tokens-as-a-Service (TaaS) Engineer to help build the systems that convert large-scale infrastructure capacity into measurable, reliable token throughput for OpenAI workloads.
In this role, you will work across performance benchmarking, tokenomics, model porting, infrastructure integration, systems tooling, and operational monitoring. You will help connect partner and first-party compute environments into OpenAI's infrastructure stack, ensuring GPU capacity can be onboarded, measured, monitored, and optimized against real workload outcomes.
Key Responsibilities
- Develop systems and tooling to measure, monitor, and improve token throughput across first-party and partner-owned compute environments.
- Support performance benchmarking, tokenomics analysis, and model porting across heterogeneous infrastructure environments.
- Build tooling to integrate external or partner infrastructure into OpenAI's internal compute, observability, and workload management systems.
- Develop and monitor operational metrics including billing, usage, SLAs, utilization, reliability, and throughput.
- Identify bottlenecks across hardware, networking, software, and workload enablement that prevent capacity from becoming productive tokens.
- Partner with compute, infrastructure, networking, finance, and operations teams to translate raw capacity into usable workload-serving capacity.
- Build dashboards, automation, and reporting systems that provide clear visibility into TaaS capacity, performance, and business outcomes.
Qualifications
- Strong software engineering background with experience building systems, tooling, automation, or infrastructure platforms.
- Experience working across compute infrastructure, distributed systems, performance engineering, or production operations.
- Ability to reason about token throughput, utilization, benchmarking, infrastructure efficiency, and workload performance.
- Comfortable integrating external systems or partner environments into internal infrastructure stacks.
- Strong analytical and debugging skills across hardware, networking, software, and operational domains.
Preferred Skills
- Experience with GPU clusters, AI infrastructure, performance benchmarking, or workload optimization.
- Familiarity with model porting, inference/training workloads, token economics, or compute efficiency analysis.
- Experience building monitoring systems for billing, usage, SLAs, utilization, or infrastructure reliability.
- Background in systems engineering, infrastructure software, observability, distributed systems, or platform engineering.
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