软件工程师，模型运行时
Software Engineer, Model Runtime
关于团队
OpenAI 的硬件部门开发适用于先进 AI 工作负载独特需求的原生 AI 芯片和系统级解决方案。在 Jalapeño 等工作的基础上，该团队正在开发下一代原生 AI 芯片和高度集成的系统，以支持前沿模型的下一次迭代。通过芯片、系统、工具和方法的协同设计，该团队帮助为 OpenAI 的超算平台交付更快、更高效且可投入生产的硬件。
关于职位
你将构建推理引擎中的模型运行时，使复杂、前沿的模型能够在 OpenAI 的定制芯片上大规模执行。运行时将位于运行在硬件上的模型和集群服务软件堆栈的上层之间，将高要求的推理工作负载转化为高效的执行，同时优化吞吐量、延迟、利用率和可靠性。
你将跨模型架构、分布式系统、编译器、内核和芯片进行工作，设计一个生产级别的运行时，其雄心与 vLLM 和 SGLang 等系统相当，但针对 OpenAI 的 AI 加速器进行了定制和优化。你的工作将决定新模型功能如何映射到平台上，以及定制芯片在生产中能多快实现有意义的性能提升。
在此职位中，你将：
- 设计并实现运行在定制芯片上的前沿模型的 LLM 推理运行时。
- 构建调度、持续批处理、内存管理、KV 缓存管理以及高性能推理的执行协调功能。
- 开发跨芯片、主机和机架的分布式执行策略，包括模型划分、通信和同步。
- 优化跨多种模型架构和服务工作负载的端到端延迟、吞吐量、内存效率和硬件利用率。
- 与内核、编译器、架构和芯片团队合作，协同设计接口并消除整个堆栈中的性能瓶颈。
- 在可靠生产运行时中启用新模型功能、执行模式、数值格式和硬件能力。
- 创建性能分析、可观测性、基准测试和性能建模工具，使运行时行为可测量且可操作。
- 调试跨越模型代码、运行时软件、通信层和硬件的复杂正确性、性能和可靠性问题。
- 将工作负载洞察转化为未来一代硅片的清晰需求。
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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 Jalapeño, 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
You will build the model runtime within the inference engine that executes complex, frontier models at scale on OpenAI’s custom silicon. The runtime will sit between models running on the hardware and the upper layers of the cluster serving software stack, translating demanding inference workloads into efficient execution while optimizing for throughput, latency, utilization, and reliability.
You will work across model architecture, distributed systems, compilers, kernels, and silicon to design a production-grade runtime comparable in ambition to systems such as vLLM and SGLang, but customized and optimized for OpenAI’s AI accelerator. Your work will shape how new model capabilities map onto the platform and how quickly custom silicon can deliver meaningful performance in production.
In this role, you will:
- Design and implement the LLM inference runtime for frontier models running on custom silicon.
- Build scheduling, continuous batching, memory management, KV-cache management, and execution orchestration for high-performance inference.
- Develop distributed execution strategies across chips, hosts, and racks, including model partitioning, communication, and synchronization.
- Optimize end-to-end latency, throughput, memory efficiency, and hardware utilization across diverse model architectures and serving workloads.
- Partner with kernel, compiler, architecture, and silicon teams to co-design interfaces and remove performance bottlenecks across the stack.
- Enable new model features, execution patterns, numerical formats, and hardware capabilities in a reliable production runtime.
- Create profiling, observability, benchmarking, and performance-modeling tools that make runtime behavior measurable and actionable.
- Debug complex correctness, performance, and reliability issues spanning model code, runtime software, communication layers, and hardware.
- Turn workload insights into clear requirements for future generations of silicon and system architecture.
You might thrive in this role if:
- Have strong systems programming experience in C++, Rust, Python, or comparable performance-oriented environments.
- Have built or optimized runtimes, distributed systems, compilers, kernels, model-serving infrastructure, or adjacent systems software.
- Understand modern LLM inference, including prefill and decode behavior, batching, KV-cache tradeoffs, and model parallelism.
- Can reason quantitatively about latency, throughput, compute intensity, memory bandwidth, communication, and utilization.
- Are comfortable profiling and debugging performance across multiple layers of a hardware-software stack.
- Can design clean abstractions while retaining the low-level control needed to extract performance from specialized hardware.
- Work effectively across model, systems, compiler, kernel, and hardware teams to drive ambiguous technical problems to closure.
- Care about production quality, including correctness, observability, reliability, maintainability, and graceful behavior at scale.
To comply with U.S. export control laws and regulations, candidates for this role may need to meet certain legal status requirements as provided in those laws and regulations.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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