> Markdown version of [/jobs/ext/2732472-software-engineer-hardware](https://www.wearedevelopers.com/jobs/ext/2732472-software-engineer-hardware). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, Hardware - **Company:** OpenAI Inc. - **Location:** San Francisco, CA, United States - **Salary:** $225,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, C++ (Programming Language), Extract Transform Load (ETL), Distributed Systems, Python (Programming Language), Systems Development Life Cycle, System Programming, Value Engineering, Scripting, High Performance Computing, Build Management, Machine Learning Operations - **Published:** September 5, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/14681910?backUrl=%2Fcareer%2F14681910%2FSoftware-Engineer-Hardware-California-San-Francisco ## About the Role * Have a deep curiosity for how large-scale systems work and enjoy making them faster, simpler, and more reliable. * Are proficient in systems programming (e.g., Rust, C++) and scripting languages like Python. * Have experience in one or more of the following areas: compiler development, kernel authoring, accelerator programming, runtime systems, distributed systems, or high-performance simulation. * Are excited to work in a fast-paced, highly collaborative environment with evolving hardware and ML system demands. * Value engineering excellence, technical leadership, and thoughtful system design. ## Description United States, California, San Francisco 3180 18th Street (Show on map) Aug 19, 2026 About the Team OpenAI's Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeno, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI's supercomputing platform. About the Role As a software engineer on the Scaling team, you'll help build and optimize the low-level stack that orchestrates computation and data movement across OpenAI's supercomputing clusters. Your work will involve designing high-performance runtimes, building custom kernels, contributing to compiler infrastructure, and developing scalable simulation systems to validate and optimize distributed training workloads. You will work at the intersection of systems programming, ML infrastructure, and high-performance computing, helping to create both ergonomic developer APIs and highly efficient runtime systems. This means balancing ease of use and introspection with the need for stability and performance on our evolving hardware fleet. This role is based in San Francisco, CA, with a hybrid work model (3 days/week in-office). Relocation assistance is available. In this role, you will: * Design and build APIs and runtime components to orchestrate computation and data movement across heterogeneous ML workloads. * Contribute to compiler infrastructure, including the development of optimizations and compiler passes to support evolving hardware. * Engineer and optimize compute and data kernels, ensuring correctness, high performance, and portability across simulation and production environments. * Profile and optimize system bottlenecks, especially around I/O, memory hierarchy, and interconnects, at both local and distributed scales. * Develop simulation infrastructure to validate runtime behaviors, test training stack changes, and support early-stage hardware and system development. * Rapidly deploy runtime and compiler updates to new supercomputing builds in close collaboration with hardware and research teams. * Work across a diverse stack, primarily using Rust and Python, with opportunities to influence architecture decisions across the training framework. ## Related Videos - [JavaScript? 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