Working at the intersection of hardware and software, you will implement state-of-the-art techniques from custom kernel development to distributed system architectures. Your work will span the entire stack,from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization.

Strong candidates will have a track record of delivering transformative GPU performance improvements in production ML systems and will be excited to shape the future of AI infrastructure alongside world-class researchers and engineers.

Responsibilities:

• Architect and implement foundational systems that power Claude
• Maximize GPU utilization and performance at unprecedented scale
• Develop cutting-edge optimizations that directly enable new model capabilities
• Dramatically improve inference efficiency
• Implement state-of-the-art techniques from custom kernel development to distributed system architectures
• Work at the intersection of hardware and software
• Span the entire stack,from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization

Requirements:

• Deep experience with GPU programming and optimization at scale
• Impact-driven, passionate about delivering measurable performance breakthroughs
• Ability to navigate complex systems from hardware interfaces to high-level ML frameworks
• Enjoy collaborative problem-solving and pair programming
• Want to work on state-of-the-art language models with real-world impact
• Care about the societal impacts of your work
• Thrive in ambiguous environments where you define the path forward

Nice to have:

• Experience with GPU Kernel Development: CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization
• ML Compilers & Frameworks: PyTorch/JAX internals, torch.compile, XLA, custom operators
• Performance Engineering: Kernel fusion, memory bandwidth optimization, profiling with Nsight
• Distributed Systems: NCCL, NVLink, collective communication, model parallelism
• Low-Precision: INT8/FP8 quantization, mixed-precision techniques
• Production Systems: Large-scale training infrastructure, fault tolerance, cluster orchestration

Representative projects:

• Co-design attention mechanisms and algorithms for next-generation hardware architectures
• Develop custom kernels for emerging quantization formats and mixed-precision techniques
• Design distributed communication strategies for multi-node GPU clusters
• Optimize end-to-end training and inference pipelines for frontier language models
• Build performance modeling frameworks to predict and optimize GPU utilization
• Implement kernel fusion strategies to minimize memory bandwidth bottlenecks
• Create resilient systems for planet-scale distributed training infrastructure
• Profile and eliminate performance bottlenecks in production serving infrastructure
• Partner with hardware vendors to influence future accelerator capabilities and software stacks

Note: The salary range for this position is $280,000-$850,000 USD per year.

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Pioneering the next generation of AI requires breakthrough innovations in GPU performance and systems engineering. As a GPU Performance Engineer, you'll architect and implement the foundational systems that power Claude and push the frontiers of what's possible with large language models. You'll be responsible for maximizing GPU utilization and performance at unprecedented scale, developing cutting-edge optimizations that directly enable new model capabilities and dramatically improve inference efficiency.

Working at the intersection of hardware and software, you'll implement state-of-the-art techniques from custom kernel development to distributed system architectures. Your work will span the entire stack—from low-level tensor core optimizations to orchestrating thousands of GPUs in perfect synchronization.

Strong candidates will have a track record of delivering transformative GPU performance improvements in production ML systems and will be excited to shape the future of AI infrastructure alongside world-class researchers and engineers.

You might be a good fit if you:

• Have deep experience with GPU programming and optimization at scale
• Are impact-driven, passionate about delivering measurable performance breakthroughs
• Can navigate complex systems from hardware interfaces to high-level ML frameworks
• Enjoy collaborative problem-solving and pair programming
• Want to work on state-of-the-art language models with real-world impact
• Care about the societal impacts of your work
• Thrive in ambiguous environments where you define the path forward

Strong candidates may also have experience with:

• GPU Kernel Development: CUDA, Triton, CUTLASS, Flash Attention, tensor core optimization
• ML Compilers & Frameworks: PyTorch/JAX internals, torch.compile, XLA, custom operators
• Performance Engineering: Kernel fusion, memory bandwidth optimization, profiling with Nsight
• Distributed Systems: NCCL, NVLink, collective communication, model parallelism
• Low-Precision: INT8/FP8 quantization, mixed-precision techniques
• Production Systems: Large-scale training infrastructure, fault tolerance, cluster orchestration

Representative projects:

• Co-design attention mechanisms and algorithms for next-generation hardware architectures
• Develop custom kernels for emerging quantization formats and mixed-precision techniques
• Design distributed communication strategies for multi-node GPU clusters
• Optimize end-to-end training and inference pipelines for frontier language models
• Build performance modeling frameworks to predict and optimize GPU utilization
• Implement kernel fusion strategies to minimize memory bandwidth bottlenecks
• Create resilient systems for planet-scale distributed training infrastructure
• Profile and eliminate performance bottlenecks in production serving infrastructure
• Partner with hardware vendors to influence future accelerator capabilities and software stacks

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The expected salary range for this position is:

Annual Salary: $280,000 - $850,000USD

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Location

San Francisco

Employment Type

Full time

Department

Scaling

Compensation

• $455K – $555K

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.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

Our Inference team brings OpenAI’s most capable research and technology to the world through our products. We empower consumers, enterprise and developers alike to use and access our start-of-the-art AI models, allowing them to do things that they’ve never been able to before. We focus on performant and efficient model inference, as well as accelerating research progression via model inference.

About the Role

We are looking for an engineering leader who wants to build and lead the worlds leading AI systems and modeling engineers who take the world's largest and most capable AI models and optimize them for use in a high-volume, low-latency, and high-availability production and research environment.

In this role, you will:

• Lead a team of engineers who are experts in working with distributed systems, with a deep understanding of model architecture, system co-design with research and production team,

• Work alongside partners in machine learning researchers, engineers, and product managers to bring our latest technologies into production.

• Work in an outcome-oriented environment where everyone contributes across layers of the stack, from infra plumbing to performance tuning.

• Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our model inference stack.

• Build tools to give us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues.

• Optimize our code and fleet of GPU’s to utilize every FLOP and every GB of GPU RAM of our hardware.

You might thrive in this role if you:

• Have an understanding of modern ML architectures and an intuition for how to optimize their performance, particularly for inference.

• Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done.

• Have at least 15 years of professional software engineering experience.

• Have or can quickly gain familiarity with PyTorch, NVidia GPUs and the software stacks that optimize them (e.g. NCCL, CUDA), as well as HPC technologies such as InfiniBand, MPI, NVLink, etc.

• Have experience architecting, building, observing, and debugging production distributed systems. Bonus point if worked on performance-critical distributed systems.

• Have needed to rebuild or substantially refactor production systems several times over due to rapidly increasing scale.

• Are self-directed and enjoy figuring out the most important problem to work on.

• Have a humble attitude, an eagerness to help your colleagues, and a desire to do whatever it takes to make the team succeed.

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.

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Location

San Francisco

Employment Type

Full time

Department

Scaling

Compensation

• $295K – $555K • 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.

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

Our Inference team brings OpenAI’s most capable research and technology to the world through our products. We empower consumers, enterprise and developers alike to use and access our start-of-the-art AI models, allowing them to do things that they’ve never been able to before. We focus on performant and efficient model inference, as well as accelerating research progression via model inference.

About the Role

We are looking for an engineer who wants to take the world's largest and most capable AI models and optimize them for use in a high-volume, low-latency, and high-availability production and research environment.

In this role, you will:

• Work alongside machine learning researchers, engineers, and product managers to bring our latest technologies into production.

• Work alongside researchers to enable advanced research through awesome engineering.

• Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our model inference stack.

• Build tools to give us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues.

• Optimize our code and fleet of Azure VMs to utilize every FLOP and every GB of GPU RAM of our hardware.

You might thrive in this role if you:

• Have an understanding of modern ML architectures and an intuition for how to optimize their performance, particularly for inference.

• Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done.

• Have at least 5 years of professional software engineering experience.

• Have or can quickly gain familiarity with PyTorch, NVidia GPUs and the software stacks that optimize them (e.g. NCCL, CUDA), as well as HPC technologies such as InfiniBand, MPI, NVLink, etc.

• Have experience architecting, building, observing, and debugging production distributed systems. Bonus point if worked on performance-critical distributed systems.

• Have needed to rebuild or substantially refactor production systems several times over due to rapidly increasing scale.

• Are self-directed and enjoy figuring out the most important problem to work on.

• Have a humble attitude, an eagerness to help your colleagues, and a desire to do whatever it takes to make the team succeed.

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.

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