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Software Engineer, Accelerators

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.

295k – 380kSan Francisco, CAFullstack EngineeringOnsite3+ YOE

About the role

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.

Skills

KernelsShardingPyTorchTpusRuntime SystemsDistributed SystemsPerformance ModelingHardware-Software Co-DesignLLMsCnns

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