# Software Engineer, Accelerators

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** Fullstack Engineering
**Salary:** $295k – $380k/yr
**Experience:** 3+ years
**Skills:** Kernels, Sharding, PyTorch, Tpus, Runtime Systems, Distributed Systems, Performance Modeling, Hardware-Software Co-Design, LLMs, Cnns
**Posted:** 2025-06-27

> Develop and optimize low-level software kernels and systems for new AI accelerator platforms to enable efficient large-scale training and inference of models like LLMs. Requires 3+ years in AI infrastructure, experience with data center-scale accelerators like TPUs, and strong systems skills.

## Job Description

## Responsibilities
- Prototype and enable OpenAI's AI software stack on new, exploratory accelerator platforms.
- Optimize large-scale model performance (LLMs, recommender systems, distributed AI workloads) for diverse hardware environments.
- Develop kernels, sharding mechanisms, and system scaling strategies tailored to emerging accelerators.
- Collaborate on optimizations at the model code level (e.g. PyTorch) and below to enhance performance on non-traditional hardware.
- Perform system-level performance modeling, debug bottlenecks, and drive end-to-end optimization.
- Work with hardware teams and vendors to evaluate alternatives to existing platforms and adapt the software stack to their architectures.
- Contribute to runtime improvements, compute/communication overlapping, and scaling efforts for frontier AI workloads.

## Requirements
- 3+ years of experience working on AI infrastructure, including kernels, systems, or hardware-software co-design.
- Hands-on experience with accelerator platforms for AI at data center scale (e.g., **TPUs**, custom silicon, exploratory architectures).
- Strong understanding of **kernels**, **sharding**, **runtime systems**, or distributed scaling techniques.
- Familiarity with optimizing **LLMs**, **CNNs**, or recommender models for hardware efficiency.
- Experience with performance modeling, system debugging, and software stack adaptation for novel architectures.
- Exposure to mobile accelerators is welcome, but experience enabling data center-scale AI hardware is preferred.
- Ability to operate across multiple levels of the stack, rapidly prototype solutions, and navigate ambiguity in early hardware bring-up phases.
- Interest in shaping the future of AI compute through exploration of alternatives to mainstream accelerators.

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