Performance And Systems Engineer in San Francisco

About OpenAI OpenAI is dedicated to ensuring that artificial general intelligence (AGI) benefits all of humanity. Our mission requires building not only world-class AI models, but also the infrastructure that enables those models to be deployed reliably, efficiently, and at global scale. As demand for AI continues to grow, we are expanding the ways OpenAI can bring high-performance inference capacity online across a diverse hardware ecosystem. About the Team The GPT Infrastructure team builds software that turns advanced inference and optimization research into production products. One focus is enabling strategic infrastructure partners and accelerator vendors to qualify and onboard new compute without a bespoke porting and optimization effort for every hardware platform. We build the control planes, APIs, secure partner-side execution environments, evaluation systems, artifact pipelines, and operational tooling that make these workflows repeatable and trustworthy. The work sits at the intersection of distributed systems, AI inference, compilers and runtimes, performance engineering, security, and external partnerships. About the Role We are seeking an experienced systems generalist who can work comfortably across the stack to help build an automated inference optimization platform. Given a workload, target hardware profile, compiler and runtime context, and a trusted verifier, the system runs durable optimization campaigns that generate, compile, execute, grade, and improve candidate kernels, runtime configurations, and serving-stack changes. You will design both the OpenAI-hosted control plane and the partner-side software that evaluates candidates on real accelerator hardware. The product must keep long-running workflows reliable, make performance results reproducible, and maintain clear trust boundaries around sensitive model and hardware information. This is a deeply cross-stack role, combining strong software engineering fundamentals with systems thinking and

About the Team OpenAI’s API Multicloud team is responsible for extending OpenAI’s API platform into strategic cloud environments, starting with AWS . The team’s mission is to distribute OpenAI’s API broadly and safely by enabling key API technologies in AWS-native environments, in close partnership with Amazon and internal teams across Codex, Research, Safety Systems, and Applied. The team is focused on bringing core developer and enterprise capabilities into cloud-native environments, including AWS-hosted Codex, model customization / post-training as a service, and new stateful runtime environments for agentic workloads. This work sits at the intersection of production ML systems, developer platforms, model behavior, and large-scale infrastructure. About the Role We’re hiring Machine Learning Engineers to build and improve the AI systems that help strategic partners adapt OpenAI models to important use cases in cloud-native environments. This role spans post-training workflows, evaluation, data pipelines, model behavior, and API/infrastructure integration. You’ll work at the boundary between partner needs and core ML systems: helping teams understand what is and isn’t working, diagnosing issues in training and evaluation workflows, and turning those learnings into improvements to the underlying platform. You should enjoy working with external technical partners, extracting the real goal from messy requests, and pushing back or reframing when the requested experiment is not the highest-leverage path. You’ll collaborate closely with Research, Applied, Safety Systems, infrastructure teams, and external technical partners to solve ambiguous model-performance problems. When you succeed, strategic partners and internal teams will be able to improve model behavior with confidence, driving measurable product improvements while the systems behind that work become more reliable, scalable, and effective over time. In this role, you will Partner with strategic customers and in

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Plaid’s mission is to unlock financial freedom for everyone by making money movement and access to financial data simple and secure. As a Software Engineer, you will design and build the systems that power how millions of people connect to their finances. You will work across the stack, from reliable backend services and APIs to intuitive applications that bring those systems to life. You will collaborate with engineers, product managers, and designers to ship products that make financial services more accessible and transparent. At Plaid, engineers take ownership early, grow quickly, and see their work reach millions of users. Responsibilities: Design & Development: Build and maintain backend services with a focus on performance, reliability and scalability. Collaboration: Work closely with product managers and other stakeholders to define and implement new features that meet product and customer needs. Code Quality: Write clean, maintainable and efficient code. Testing & Debugging: Develop automated tests to ensure the quality and reliability of the codebase. Troubleshoot and resolve issues. Engage in hands-on coding and architectural design, setting and maintaining high technical standard

About the Team The Foundations Research team works on high-risk, high-reward ideas that could shape the next decade of AI. Our goal is to advance the science and data that enable our training and scaling efforts, with a particular focus on future frontier models. Pushing the boundaries of data, scaling laws, optimization techniques, model architectures, and efficiency improvements to propel our science. The Search team sits within Foundations, building agentic search by co-designing model–system interfaces with the core search stack (serving, indexing, retrieval) to translate model intent into reliable, real-world actions. Operating at the frontier of AI and information retrieval, the team develops large-scale systems that transform and index vast corpora, enabling models to reason over global knowledge and act dependably. In close partnership with researchers, we rapidly bring modeling breakthroughs into production and redefine how intelligent systems discover, retrieve, and synthesize information at planetary scale. About the Role We’re looking for a Software Engineer focused on building and scaling retrieval systems. You’ll work with a team of researchers and engineers to develop infrastructure that enables models to retrieve and act on the right information at the right time. This includes designing and operating indexing systems, retrieval pipelines, and serving layers. This work supports retrieval across OpenAI products and research, with direct impact on system performance, reliability, and scale. Responsibilities Build and scale retrieval infrastructure across indexing, serving, and query execution. Develop low-latency, high-throughput systems for real-time model interaction. Partner with research to productionize embedding and retrieval techniques. Support dense, sparse, and hybrid retrieval pipelines. Own system performance, reliability, and observability at scale. Collaborate across Pretraining, Inference, and Product teams to integrate retrieval end-to-e

About the Team OpenAI's Professional Services team helps organizations move from AI ambition to durable production outcomes. We partner with customers on complex deployments and build the strategy, commercial models, operating mechanisms, and delivery capacity needed to realize value from OpenAI's models and products. The team works across Go-to-Market, Forward Deployed Engineering, Technical Success, Finance, Product, Legal, Data, Systems, and delivery partners. We are building a services motion that is customer-centered, commercially rigorous, operationally scalable, and deliberately connected to product adoption. About the Role We are seeking a senior GTM Strategy & Operations professional to build and scale the operating system for OpenAI's Professional Services business. This is a foundational, hands-on individual contributor role at the intersection of business strategy, finance, go-to-market, and delivery. You will turn ambiguous questions—what we offer, how we price and package it, how we plan capacity, and how we measure performance—into clear decisions and repeatable mechanisms. You will own business planning, pricing and packaging, forecasting and modeling, management reporting, and cross-functional strategic initiatives. You will build integrated views of demand, staffing, revenue, margin, and delivery performance; replace one-off analyses with durable processes; and create operating cadences that help leaders act early. You will be a trusted partner to Professional Services, GTM, FDE, Technical Success, Finance, Product, Legal, Revenue Operations, and Data leaders. The right person combines direct Professional Services judgment with rigorous analytics, executive communication, and the willingness to build the model, process, or dashboard themselves. You’ll be responsible for: Define and drive the business and GTM strategy for Professional Services, including target customer needs, offer portfolio, positioning, pricing and packaging, partner motions,

About the Team Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve. Our work focuses on three pillars: high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long-lived, job-specific and user-provided processes. We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products). About the Role As a Training Performance Engineer, you’ll drive efficiency improvements across our distributed training stack. You’ll analyze large-scale training runs, identify utilization gaps, and design optimizations that push the boundaries of throughput and uptime. This role blends deep systems understanding with practical performance engineering — analyzing GPU kernel performance, collective communication throughput, investigating I/O bottlenecks, and sharding our models so we can train them at massive scale. You’ll help ensure that our clusters are running at peak performance, enabling OpenAI to train larger, more capable models with the same compute budget. This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees. In this role, you will: Profil

About the Team The Future of Computing Research team is an applied research team within OpenAI’s Consumer Devices group. We study how AI systems perceive people and their surroundings, and we turn that research into capabilities for future products. Our work spans machine learning, sensing, and hardware, with a focus on building systems that work beyond controlled environments. About the Role We’re looking for a machine learning engineer to help shape how future AI systems understand the physical world and the people in it. The role focuses on multimodal perception and authentication, bringing together signals from cameras, microphones, and other sensors. You’ll work with specialized perception models and larger multimodal models, and partner with hardware, firmware, software, and product teams to bring new research into real-world systems. This role is based in San Francisco. We work in the office three days per week and offer relocation assistance. In this role, you will: Research and develop multimodal perception and authentication methods across visual, audio, and other sensing signals. Explore how specialized perception models and larger multimodal models can work together. Design data, training, and evaluation approaches that improve performance in real-world conditions. Study model behavior, robustness, and failure modes across sensing, data, and deployment environments. Integrate and validate new capabilities in real-time or resource-constrained systems. Work with hardware, firmware, software, and product teams to turn research into working systems. You might thrive in this role if you: Have a strong background in computer vision, audio or speech machine learning, multimodal learning, or sensing. Have experience developing specialized machine learning models, larger multimodal models, or both. Have brought research ideas into practical systems, prototypes, or products. Know how to design experiments, build evaluations, and investigate model behavior. Have wo

About the Team The Software Engineering Firmware team builds reliable, high-performance systems on custom hardware. We work closely with hardware engineers to design, optimize, and ship software that bridges cutting-edge devices and real-world constraints like memory, power, and latency. Our work spans early prototyping through product launch, ensuring that our embedded platforms are robust, efficient, and production-ready. About the Role As a Firmware Engineer , you will design, implement, and debug software for embedded devices. You’ll own low-level bring-up, write production C/C++ code, and partner closely with hardware teams to deliver reliable, high-performance systems. We’re looking for engineers with deep embedded expertise, strong debugging skills, and a passion for building systems that perform under real-world conditions. This role is based in San Francisco, CA . We use a hybrid work model of four days in the office per week and offer relocation assistance to new employees. In this role, you will: Design, implement, and debug software for embedded devices. Contribute to defining software requirements, interfaces, and test plans. Bring up and debug new boards. Analyze performance, memory, and power profiles and implement optimizations. Investigate field issues, perform root-cause analysis, and deliver robust fixes. Foster good software engineering practices. You might thrive in this role if you: Have deep experience shipping embedded systems (around 10+ years). Are proficient in C and C++. Are familiar with embedded toolchains, operating systems, and debugging tools. Have experience with both rapid prototyping and scalable product development. (Nice to have) Have experience with Zephyr RTOS. (Nice to have) Have worked with networking/wireless stacks (BLE, Wi-Fi). (Nice to have) Have experience with robotic system bring-up or Linux kernel development. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose arti

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 are seeking a senior Actuator Electromagnetic Design Engineer to lead the development of custom electromechanical actuators for advanced robotic systems. You will own actuator development from early architecture and concept generation through prototype validation and system integration, partnering closely with mechanical, electrical, controls, firmware, reliability, and manufacturing teams. This role focuses on the design, integration, and validation of precision electromechanical systems, including motors, transmissions, sensing, structural components, and thermal architectures. You will help drive actuator development across the full engineering lifecycle while establishing scalable design, test, and integration practices for future robotic platforms. This role is based in San Francisco, CA, and requires in-person presence 4 days a week. In this role, you will: Lead the architecture, design, and integration of custom robotic actuators, including the design, simulation, integration and sourcing of custom electromagnetic components. Define actuator requirements and system-level trade studies around torque density, bandwidth, efficiency, thermal performance, inertia, reliability, manufacturability, and cost. Design precision electromechanical assemblies with strong attention to tolerances, alignment, load paths, thermal expansion, sealing, wear, and serviceability. Drive actuator integration into robotic systems, partnering closely with controls, firmware, electrical, and robotics software teams to optimize closed-loop performance. Devel

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 are seeking a Senior Actuator Design and Integration Engineer to lead the development of custom electromechanical actuators for advanced robotic systems. You will own actuator development from early architecture and concept generation through prototype validation and system integration, partnering closely with mechanical, electrical, controls, firmware, reliability, and manufacturing teams. This role focuses on the design, integration, and validation of precision electromechanical systems, including motors, transmissions, sensing, structural components, and thermal architectures. You will help drive actuator development across the full engineering lifecycle while establishing scalable design, test, and integration practices for future robotic platforms. This role is based in San Francisco, CA, and requires in-person presence 4 days a week. In this role, you will Lead the architecture, design, and integration of custom robotic actuators, including motors, transmissions, sensing, thermal systems, structural components, and packaging. Define actuator requirements and system-level trade studies around torque density, bandwidth, efficiency, thermal performance, backdrivability, inertia, reliability, manufacturability, and cost. Design precision electromechanical assemblies with strong attention to tolerances, alignment, load paths, thermal expansion, sealing, wear, and serviceability. Drive actuator integration into robotic systems, partnering closely with controls, firmware, electrical, and robotics software teams to optimize closed-loop pe

About the role We’re looking for an engineering manager to lead a team building software systems that detect and prevent harmful misuse of frontier AI models—before incidents occur. This is a builder’s role: you’ll lead engineers shipping production services, detection pipelines, and mitigation mechanisms that protect frontier model integrity and reduce high-severity misuse risk. While this work intersects with frontier model development, security and risk, we’re explicitly seeking someone with a software engineering foundation who is comfortable building reliable systems that can operate at billions of users scale. In this role you will: Lead a team of software engineers building detection + mitigation systems for frontier model misuse, with an emphasis on model IP protection / distillation detection and emerging risk surfaces from autonomous agents. Set the technical roadmap and execution strategy: prioritize, design, ship, iterate, measure impact. Build production systems: services, pipelines, tooling, instrumentation, and automation that scale with frontier model usage. Partner deeply with Research and Product to translate evolving model capabilities into concrete tests, signals, and mitigations that can be deployed at scale. Drive strong engineering fundamentals: architecture, reliability, monitoring, performance, and operational excellence. Hire and grow an exceptional team across backend, data systems, and applied ML engineering domains as needed. Anticipate what breaks at scale as agentic workflows become more capable. You might thrive in this role if you: Experience building systems in adversarial, fast-evolving environments Are comfortable with ambiguity and novelty Have experience adjacent to security (e.g., abuse prevention, fraud, integrity, platform defense, auth/identity, malware/spam, adversarial environments) Communicate clearly and build trust quickly with senior stakeholders—pragmatic, collaborative, and calm under scrutiny. Significant experience

Why join us Brex is the intelligent finance platform that enables companies to spend smarter and move faster in more than 200 markets. By combining global corporate cards and banking with intuitive spend management, bill pay, and travel software, Brex enables founders and finance teams to accelerate operations, gain real-time visibility, and control spend effortlessly. Brex’s AI-native automation and world-class service eliminate manual expense and accounting tasks for customers so they can focus on what matters most. Tens of thousands of the world's best companies run on Brex, including DoorDash, Coinbase, Robinhood, Zoom, Plaid, Reddit, and SeatGeek. Working at Brex allows you to push your limits, challenge the status quo, and collaborate with some of the brightest minds in the industry. We’re committed to building a diverse team and inclusive culture and believe your potential should only be limited by how big you can dream. We make this a reality by empowering you with the tools, resources, and support you need to grow your career. Engineering at Brex Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders. Where you’ll work This role will be based in our San Francisco Office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work! What you’ll do This opportunity is on our Product Data Platform (PDP) team, which builds and operates the high-performance dat

About the Team Training Runtime designs the core distributed machine-learning training runtime that powers everything from early research experiments to frontier-scale model runs. With a dual mandate to accelerate researchers and enable frontier scale, we’re building a unified, modular runtime that meets researchers where they are and moves with them up the scaling curve. Our work focuses on three pillars: high-performance, asynchronous, zero-copy tensor and optimizer-state-aware data movement; performant, high-uptime, fault-tolerant training frameworks (training loop, state management, resilient checkpointing, deterministic orchestration, and observability); and distributed process management for long-lived, job-specific and user-provided processes. We integrate proven large-scale capabilities into a composable, developer-facing runtime so teams can iterate quickly and run reliably at any scale, partnering closely with model-stack, research, and platform teams. Success for us is measured by raising both training throughput (how fast models train) and researcher throughput (how fast ideas become experiments and products). About the Role As a Training: ML Framework Engineer, you will work on improving the training throughput for our internal training framework, while enabling researchers to experiment with new ideas. This requires good engineering (for example designing, implementing, and optimizing state-of-the-art AI models), writing bug-free machine learning code (surprisingly difficult!), and acquiring deep knowledge of the performance of supercomputers. In all the projects this role pursues, the ultimate goal is to push the field forward. We’re looking for people who love optimizing performance, understanding distributed systems, and who cannot stand having bugs in their code. Since our training framework is used for large runs with massive numbers of GPUs, performance improvements here will have a large impact. This role is based in San Francisco, CA. We use a

About the Team OpenAI's mission is to ensure that artificial general intelligence benefits all of humanity. The Consumer Devices team is building a new generation of AI-powered products that seamlessly integrate hardware and software to create intuitive, transformative experiences. We bring together experts across embedded systems, machine learning, hardware, design, and product engineering to develop products at the intersection of AI and consumer technology. About the Role OpenAI is seeking a System Power Engineer to characterize, measure, and optimize power consumption across our embedded hardware products. In this role, you will work closely with Electrical Engineering and system software teams to build power test automation, measure subsystem-level power usage, and drive improvements that directly impact battery life, thermal behavior, charging performance, and system reliability. You will help establish the methodologies and metrics used to understand and improve power efficiency across real-world product experiences, from controlled lab environments to representative day-in-the-life usage scenarios. This role requires hands-on experience with embedded hardware platforms, power instrumentation, and the analysis of power profiles and system behavior. This role is based in San Francisco, CA. We use a hybrid work model of four days per week in the office and one day working remotely. Relocation assistance is available for new hires. In this role, you will: Define and develop power testing automation to evaluate system behavior across a range of workloads and operating conditions. Measure subsystem-level power consumption using power breakout probes and other lab instrumentation. Develop and execute power characterization tests spanning basic workloads, complex mixed-use scenarios, and representative day-of-use experiences. Partner closely with Electrical Engineers to identify opportunities to improve system power efficiency. Collaborate with software engineering

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 need