Platform Engineer – AI Inference & Optimization

OpenAI

California (MO)

On-site

USD 180,000 - 240,000

Full time

14 days+

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Job summary

OpenAI seeks a software engineer to build a platform that qualifies and optimizes inference workloads across heterogeneous compute environments. You will develop OpenAI-hosted services and secure partner-side software for long-lived optimization workflows.

You will collaborate across model architecture, compilers, runtimes, and distributed execution to turn research prototypes into reliable, auditable infrastructure with strong observability and security.

Qualifications

  • Experience building distributed systems and production services.
  • Ability to design APIs and durable workflows for large-scale backends.
  • Proficiency in one or more systems languages and modern tooling.

Responsibilities

  • Design, build, and operate APIs and control-plane services for long-running workload campaigns.
  • Build secure partner-side execution and evaluation software for accelerator hardware.
  • Integrate model workloads, hardware profiles, compilers, runtimes, and serving engines.
  • Develop evaluation systems for fidelity, latency, throughput, and cost.
  • Automate generation and optimization of kernels and runtime configurations.
  • Diagnose performance and correctness issues across software and hardware boundaries.
  • Build artifact-management, provenance, regression-testing, and qualification workflows.
  • Turn research workflows into reliable product surfaces with clear interfaces.

Skills

Distributed systems
APIs & workflows
Python/C++/Go/Rust
Linux & containers
Profiling & benchmarking
Cross-team ownership

Tools

CUDA
ROCm
Triton
LLVM
MLIR

Job description

OpenAI seeks a software engineer to build a platform that qualifies and optimizes inference workloads across heterogeneous compute environments. You will develop OpenAI-hosted services and secure partner-side software for long-lived optimization workflows.

You will collaborate across model architecture, compilers, runtimes, and distributed execution to turn research prototypes into reliable, auditable infrastructure with strong observability and security.

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