# Software Engineer, Model Runtime **Company**: OpenAI **Location**: San Francisco **Work arrangement**: hybrid **Experience**: senior **Job type**: Full time **Salary**: $266K - $445K **Category**: Engineering **Industry**: Technology **Wikidata**: https://www.wikidata.org/wiki/Q124605186 **Apply**: https://jobs.ashbyhq.com/openai/ec317080-e2d2-4a73-93e6-e0a9ae6fdf96?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_4842b8bb-79b ## Description ## Compensation $266K – $445K • Offers Equity The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits. - Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts - Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit) - 401(k) retirement plan with employer match - Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks) - Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees - 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law) - Mental health and wellness support - Employer-paid basic life and disability coverage - Annual learning and development stipend to fuel your professional growth - Daily meals in our offices, and meal delivery credits as eligible - Relocation support for eligible employees - Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided. ## 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. ## Responsibilities - 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. ## Requirements - 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. ## Skills ### Required - C++ - Rust - Python - systems programming - distributed systems - compilers - kernels - model-serving infrastructure ### Nice to have - LLM inference - prefill and decode behavior - batching - KV-cache tradeoffs - model parallelism --- Source: [Apply at jobs.ashbyhq.com](https://jobs.ashbyhq.com/openai/ec317080-e2d2-4a73-93e6-e0a9ae6fdf96?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)