# Software Engineer, Kernel Performance & AI Tooling

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** DevOps / SRE
**Salary:** $266k – $445k/yr
**Skills:** Kernel Development, Performance Optimization, Gpus, Cpus, Compilers, Developer Tooling, Observability, Profiling, Ai-Assisted Systems, Reinforcement Learning, Dsls, Program Synthesis, Accelerator Architecture, Hardware-Software Co-Design, Debugging Infrastructure
**Posted:** 2026-04-17

> Develops kernel performance optimizations, AI-assisted tooling, and observability infrastructure for AI-native hardware. Requires strong low-level systems experience, kernel/accelerator expertise, and familiarity with AI workflows for engineering acceleration.

## Job Description

## Responsibilities
- Build developer tooling and workflows that make kernel development and performance optimization faster, more scalable, and easier to debug, integrate, and deploy.
- Develop observability, diagnostics, and validation infrastructure that makes AI-assisted optimization systems more interpretable, reliable, and effective.
- Optimize production kernels end to end by formulating optimization problems, running search loops, analyzing bottlenecks, debugging generated implementations, and landing improvements into production.
- Design abstractions, interfaces, and automation systems that accelerate kernel optimization, correctness validation, and hardware-software co-design.
- Improve AI-assisted optimization systems for specialized tasks through better datasets, evaluations, benchmarking, and research infrastructure.
- Partner across research and engineering teams to turn new ideas into practical systems spanning production needs and long-term infrastructure strategy.

## Requirements
- Strong systems or tooling engineering experience, with a background in low-level software, performance optimization, or infrastructure.
- Experience with developer tooling, debugging infrastructure, profiling, observability, or workflow design for technical users.
- Depth in kernel development, accelerator architecture, compiler systems, or related performance-critical domains.
- Familiarity with AI-assisted systems, agentic workflows, post-training, or reinforcement learning for engineering or research applications.
- Strong experimental judgment, comfort with ambiguity, and the ability to move fluidly between research exploration and production execution.
- Interest in compilers, DSLs, program synthesis, or AI for systems.

## Preferred
- Strong systems and tooling engineer with real depth in kernels and accelerators.
- Comfortable working across software and hardware boundaries, can reason deeply about performance, abstractions, and system design.
- Hands-on experience optimizing code for GPUs, high-performance CPUs, or custom accelerators.
- View AI not as the end product, but as a force multiplier for engineering productivity and system optimization.

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