Software Engineer, Inference - Performance Optimization

About the Team The Scaling team is responsible for the architectural and engineering backbone of OpenAI’s infrastructure. We design and deliver advanced systems that support the deployment and operation of cutting-edge AI models. Our work spans system software, networking, platform architecture, fleet-level monitoring, and performance optimization. About the Role We’re hiring an SW Engineer to enable production workloads and end-to-end testing on new platforms. This role will include creating new test harnesses and platform stress benchmarks, porting existing inference and training workloads to new, sometimes early-access, systems/hardware, analyzing performance and bottlenecks, and characterizing the end-to-end behavior of new systems (compute, comms, storage, control plane, and failure modes). Key Responsibilities Port and validate key inference and training workloads on new platforms/SKUs as they arrive; drive correctness, performance, and stability to an internal readiness bar. Build a suite of benchmarks and stress tests that capture real E2E behavior of our workloads by exercising all aspects of a system, including CPU, GPU, memory subsystem, frontend, scale-up, and scale-out networking (including WAN traffic, NVlink and RDMA collectives), storage, thermals, and any other relevant parts. Deep-dive performance on distributed training/inference: Collective performance and tuning (across NCCL/RCCL and internal libraries) Overlap of compute/communication, kernel-level bottlenecks, memory bandwidth and scheduling effects Create repeatable test harnesses that run in CI / lab environments and produce actionable outputs (pass/fail, performance score, regression detection). Partner with systems + fleet bring-up engineers to ensure the platform is not only stable and performant, but also operationally usable and scalable (containerization, K8s integration, telemetry hooks, failure triage loops). Work cross-functionally with vendors and internal stakeholders by producing

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE Are you passionate about advancing the application of artificial intelligence? We are looking for a Software Engineer focused on ML performance to join our dynamic team. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to the exciting field of LLM Inference. If you are a backend engineer who thrives on making things faster and is excited about open-source ML models, we look forward to your application. EXAMPLE INITIATIVES You'll get to work on these types of projects as part of our Model Performance team: Baseten Embeddings Inference: The fastest embeddings solution available The Baseten Inference Stack Driving model performance optimization RESPONSIBILITIES Implement, refine, and productionize cutting-edge techniques (quantization, speculative decoding, kv cache reuse, chunked prefill and LoRA) for ML model inference and infrastructure. Deep dive into underlying codebases of TensorRT, PyTorch, TensorRT-LLM, vllm, sglang, CUDA, and other libraries to debug ML performance issues. Apply and scale optimization techniques across a wide range of ML models, particularly large language models. Collaborate with a diverse team to design and implement innovative solutions. Own projects from idea to production. REQUIREMENTS Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field. Experience with one

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE We’re seeking a GPU Kernel Engineer to join our team at the cutting edge of AI acceleration, where your code directly impacts the performance of state-of-the-art machine learning models. As a GPU Kernel Engineer, you'll craft the foundation that powers modern AI workloads, optimizing every microsecond of computation to enable breakthrough applications. You'll work in a fast-paced, intellectually stimulating environment where technical excellence is paramount and your contributions directly influence production systems serving millions of users across numerous products. This role offers exceptional growth potential for engineers passionate about low-level optimization and high-impact systems work. EXAMPLE INITIATIVES You'll get to work on these types of projects as part of our Model Performance team: Baseten Embeddings Inference: The fastest embeddings solution available The Baseten Inference Stack Driving model performance optimization RESPONSIBILITIES Core Engineering Responsibilities Design and implement high-performance GPU kernels for key ML operations, including matrix multiplications, attention mechanisms, and mixture-of-experts routing Write and optimize code using CUDA, PTX assembly, and architecture-specific techniques Apply advanced performance optimization methods such as memory coalescing, warp-level programming, tensor core acceleration, and compute/memory overlap Performance & Innovation Impl

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE As an Infrastructure Software Engineer at Baseten, you'll build and maintain components of our ML inference platform that powers production AI applications. You'll contribute to the core infrastructure, enabling developers to deploy, scale, and monitor ML models with high performance. EXAMPLE INITIATIVES You'll get to work on these types of projects as part of our Infrastructure team: Multi-cloud capacity management Inference on B200 GPUs Multi-node inference Fractional H100 GPUs for efficient model serving RESPONSIBILITIES Develop infrastructure components for our ML inference platform using Python and Go Implement and maintain Kubernetes deployments for model serving Contribute to our inference orchestration layer for model deployments Build and enhance monitoring systems for model performance metrics Implement efficient resource management solutions for ML workloads Support infrastructure automation to improve ML deployment workflows Work closely with team members to implement technical solutions Help balance performance optimization with system reliability Participate in technical discussions around infrastructure improvements Learn and apply infrastructure best practices REQUIREMENTS Bachelor's degree or higher in Computer Science or related field Proficient coding abilities in one or more popular programming or scripting languages; Go proficiency is a plus Working knowledge of Kubernetes and containeriza

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 On the Accelerators team, you will help OpenAI evaluate and bring up new compute platforms that can support large-scale AI training and inference. Your work will range from prototyping system software on new accelerators to enabling performance optimizations across our AI workloads. You’ll work across the stack, collaborating with both hardware and software aspects - working on kernels, sharding strategies, scaling across distributed systems, and performance modeling. You'll help adapt OpenAI's software stack to non-traditional hardware and drive efficiency improvements in core AI workloads. This is not a compiler-focused role, rather bridging ML algorithms with system performance - especially at scale. In this role, you will: Prototype and enable OpenAI's AI software stack on new, exploratory accelerator platforms. Optimize large-scale model performance (LLMs, recommender systems, distributed AI workloads) for diverse hardware environments. Develop kernels, sharding mechanisms, and system scaling strategies tailored to emerging accelerators. Collaborate on optimizations at the model code level (e.g. PyTorch) and below to enhance performance on non-traditional hardware. Perform system-level performance modeling, debug bottlenecks, and drive end-to-end optimization. Work with hardware teams and vendors to evaluate alternatives to existing platforms and adapt the software stack to their architectures. Contribute to runtime improvements, compute/communication over

About the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples’ lives. About the Role We’re looking for a GPU Inference Engineer to contribute to improvements in model serving efficiency for our Robotics research. This is a high-impact role where you’ll drive initiatives to optimize inference performance and scalability. You’ll also be engaged in model design, to help assist our researchers in developing inference-friendly models. This role is critical to scaling the team’s broader goals - it will directly enable leadership to focus on higher-leverage initiatives by building a stronger technical foundation. In this role you will: Perform engineering efforts focused on improving model serving, inference performance, and system efficiency Drive optimizations from a kernel and data movement perspective to improve system throughput and reliability Partner closely with research and product teams to ensure our models perform effectively at scale Design, build, and improve critical serving infrastructure to support Robotics growth and reliability needs You might thrive in this role if you: Have deep expertise in model performance optimization, particularly at the inference layer Have a strong background in kernel-level systems, data movement, and low-level performance tuning Are excited about scaling high-performing AI systems that serve real-world, multimodal workloads Can navigate ambiguity, set technical direction, and drive complex initiatives to completion This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. About OpenAI OpenAI i