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Software Engineer, Model Runtime

OpenAI

Software Engineer, Model Runtime is a still-open role at OpenAI on Jobrow. Location: Hybrid · San Francisco · California · United States. Listed pay: $266k–$445k. Sourced from the company's public Ashby board. First seen 2 months ago; last confirmed 9 days ago. Apply on the employer ATS — Jobrow is not the employer.

Pay
$266k–$445k
Workplace
Hybrid
Location
San Francisco · California · United States
First seen
2 months ago
Last seen
9 days ago
Board
Ashby
Apply

About this role

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads.

  • Function: Engineering
  • Seniority: mid

Location & pay

Hybrid · San Francisco · California · United States Listed pay on Jobrow: $266k–$445k.

Source

Jobrow reads the public Ashby board for OpenAI. First seen 2 months ago; last seen 9 days ago. We are not the employer — apply only on the company ATS.

Posting

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 s

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