Job description

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 cdn.openai.com. 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. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form form.asana.com. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link form.asana.com. OpenAI Global Applicant Privacy Policy cdn.openai.com At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Requirements

Must-haves

About the Team
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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmrustpython

Platform: ashby
Effort: quick