Power Integrity Engineer in United States

About the Team OpenAI’s Industrial Compute organization is building the infrastructure required to support the next generation of frontier AI systems. Through a combination of strategic partnerships and self-built data center campuses, we are scaling the power, cooling, electrical, mechanical, and controls infrastructure needed to deliver compute at unprecedented scale. The Commissioning organization is responsible for ensuring this infrastructure is safely tested, validated, integrated, and transitioned into reliable operations. For our self-build campuses, the team operates through a hybrid delivery model: OpenAI provides commissioning leadership, discipline ownership, governance, and project integration, while commissioning partners provide field and test engineering capacity to support inspections, startup, testing, and turnover. About the Role We are seeking a Commissioning Project Lead to own the commissioning strategy and execution for a large-scale, self-build data center project. You will lead the overall commissioning program from early construction planning through startup, functional testing, integrated systems testing, and final turnover. You will establish the commissioning execution plan, integrate commissioning activities into the master project schedule, coordinate multidisciplinary readiness, and lead the vendor commissioning partners providing field and test engineering capacity. This role serves as the primary commissioning interface to project leadership, construction management, contractors, equipment vendors, operations, and commissioning partners. You will be responsible for creating clarity across organizations, identifying readiness and schedule risks early, and ensuring the facility progresses through testing and turnover against clearly defined acceptance criteria. The role will initially support planning and coordination in a hybrid capacity and transition to full-time onsite presence as construction, inspections, startup, testing, and t

About the Team The Finance Platform & Technology team builds and scales the systems and data architecture that power OpenAI’s core financial operations. We enable business agility, compliance, and operational excellence across procure-to-pay, quote-to-cash, supply chain, financial planning, and asset management. We partner with Procurement, Accounting, Tax, Legal, Security, Data, and Engineering to modernize workflows through thoughtful platform design, reliable integrations, scalable automation, and trusted data. About the Role As a Business Systems Lead for Procure-to-Pay, you will be a hands-on engineer who designs, builds, and operates the integrations and first-party applications that power OpenAI’s procurement workflows. You will translate business needs into secure, scalable software, APIs, data flows, and automation across Oracle Fusion, Zip, and connected platforms. You will build the future of buying at OpenAI using OpenAI’s own technology, from guided intake and approval experiences to supplier onboarding, purchasing, receiving, invoicing, and downstream financial data flows. You will own the technical roadmap and support model for these capabilities, improving today’s platforms while deciding where to integrate, configure, or build as OpenAI scales. Your core strength will be software and integration engineering. You will personally write code, troubleshoot cross-system failures, and take solutions through testing, deployment, and production support. You will also make targeted functional configurations in procurement platforms and partner with functional specialists on deeper process and module design. 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. In this role, you will: Design, build, and operate integrations across Oracle Fusion, Zip, and connected systems using APIs, events, messaging, and batch interfaces where appropriate. Build first-party

About the Team: The OpenAI API team builds the foundation that enables every developer to harness OpenAI’s models safely, reliably, and at scale. We design and operate the systems that power model serving, API access, billing, developer tooling, and enterprise integrations—forming the connective tissue between OpenAI’s research breakthroughs and real-world products. Our mission is to make it effortless for anyone to build with OpenAI technology. We’re responsible for the infrastructure and product layers that allow millions of developers to integrate GPT models, fine-tune behavior, manage data, and deliver transformative experiences to their users. We collaborate across product, research, and engineering teams to ensure that innovation in model capabilities translates directly into value for customers. The API team spans multiple disciplines, including product management, infrastructure engineering, developer experience, and data systems. We care deeply about reliability, scalability, and simplicity—creating tools that let developers focus on their ideas while we handle the complexity of running world-class AI systems. About the Role: We are seeking an experienced Product Manager to define and scale the construction of our data processing, data privacy, billing, and access controls products. You will set strategy and execute on projects like expanding our regional data processing footprint, enabling new inference caching controls in the API or building APIs that make it easier for organizations to manage their spend limits. You will also define the strategy and ship foundational capabilities that ensure customers use OpenAI products securely, privately, and with enterprise-grade controls. This role partners deeply with engineering, security, legal, compliance, finance and leadership to deliver high-trust, enterprise-grade systems. 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 n

As one of the technology industry's most desirable employers, NVIDIA has been redefining accelerated computing, computer graphics and leading the Artificial Intelligence revolution. NVIDIA's innovation is fueled by its great technology—and amazing people. We are seeking a Senior Silicon and System Product Lead to influence, innovate and take our next generation products to the market. As part of the Silicon Solutions Team, we architect and deliver groundbreaking system solutions that integrate all aspects of the system from silicon design, software design to operations and final deployment in multiple market segments that NVIDIA serves. This position offers an unique opportunity to collaborate with multiple organizations in the company and grow your career in a high impact role. We need a passionate, hard-working and creative individual to lead the products all the way from market analysis to delivering the features on the final product. What you'll be doing: Drive product performance and power targets, trade-off features/configurations and provide innovative solutions to complex silicon and system level problems. Evaluate new market segments and use cases; translate market requirements to engineering problem statements and metrics. Innovate Performance, power, yield and quality optimizations and features for the world’s fastest power-shipping products in the GPU and SoC market segments spanning gaming, automotive, datacenter and DL/AI. Develop methodologies and requirements for multi-functional teams to drive silicon and system product features to production. Incorporate productization feedback to improve the next generation. Lead the team for feature requirements and schedule from architecture to silicon phase of projects. Work alongside system architects, designers, marketing teams, chip and board designers, software/firmware engineers, HW/S

Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. About the role For years, avatars have been something you watch. You write a script, we render a video, someone presses play. That model has taken AI video a long way but it has a ceiling. A video can't answer a question. It can't read the room. It can't role-play a tough conversation and adapt when you push back. We're changing that. Interactive Avatars listen, talk, and respond in real time; inside your product, your website, or your internal tools. You bring the logic and the language model; we power the avatar layer: the speech, the expression, the listening, the sense that there's someone actually there. It's early. Interactive Avatars is in closed beta today, and the teams building on it are pushing it into places we didn't expect - AI sales reps, always-on support agents, interactive trainers, onboarding guides. We're hiring a Product Manager to own it and take it from beta to a platform that thousands of developers build on. This is a role with real scope. You'll help define what a real-time avatar API should be, the surface developers integrate against, the experience end users feel, and the commercial model that makes it a business. You'll work directly with the engineering and research teams building

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role As a Product Manager at Ramp, you will be responsible for the vision, strategy, and roadmap for Ramp’s international payments products. You will lead a core team of engineers and designers to build and scale global money movement capabilities that enable our customers to pay vendors, employees, and partners around the world. You’ll own a critical set of customer and business problems at the intersection of payments, compliance, risk, and FX — driving end-to-end product vision, strategy, and execution to power Ramp’s global expansion and meet ambitious company goals. Your scope will include: Cross-border payments & FX – Own the experience and infrastructure for sending and receiving international payments across currencies and geographies, including FX pricing, rate transparency, settlement speed, and reliability across multiple payment rails. Global partner ecosystem – Evaluate, integrate, and manage banking, FX, and local payment service partners to expand Ramp’s coverage, improve unit economics, and unlock new corridors and capa

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior Software Engineer Job Overview: Responsible for the analysis, design, development, testing, and delivery of secure, scalable software solutions. Define requirements for new applications and customization adhering to Mastercard standards, processes, and best practices. Develop, customize, and test applications to integrate to Mastercard specifications. Provide leadership, mentoring, and technical training to other team members. Major Accountabilities • Plan, design, architect, and develop secure, scalable, and maintainable technical solutions and alternatives to meet business requirements in adherence with Mastercard standards, processes, and best practices • Lead day-to-day system development and maintenance activities of the team to meet service level agreements (SLAs) and create solutions with a high level of innovation, cost effectiveness, quality, reliability, and faster time to market. • Accountable for the full systems development life cycle including creating high-quality requirements documents, use cases, designs, and other technical artifacts including but not limited to detailed test strategies, performance benchmarking, release rollout and deployment plans, contingency/back-out plans, feasibility studies, cost and time analysis, and detailed estimates. • Design, develop, test, dep

The Fleet team at OpenAI supports the computing environment that powers our cutting-edge research and product development. We oversee large-scale systems that span data centers, GPUs, networking, and more, ensuring high availability, performance, and efficiency. Our work enables OpenAI’s models to operate seamlessly at scale, supporting both internal research and external products like ChatGPT. We prioritize safety, reliability, and responsible AI deployment over unchecked growth. About the Role The Software Engineer, Operating Systems & Orchestration will focus on building systems to manage hardware, configurations, vendors, and the people interacting with our infrastructure. You will design and develop solutions that integrate individual nodes and servers into unified clusters, directly contributing to advancing AI research by streamlining the overall research user experience. 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. In this role, you will: Design and build systems to manage both cloud and bare-metal fleets at scale. Develop tools that integrate low-level hardware metrics with high-level job scheduling and cluster management algorithms. Leverage LLMs to coordinate vendor operations and optimize infrastructure workflows. Automate infrastructure processes, reducing repetitive toil and improving system reliability. Collaborate with hardware, infrastructure, and research teams to ensure seamless integration across the stack. Continuously improve tools, automation, processes, and documentation to enhance operational efficiency. You might thrive in this role if you: Have strong software engineering skills with experience in large-scale infrastructure environments. Possess broad knowledge of cluster-level systems (e.g., Kubernetes, CI/CD pipelines, Terraform, cloud providers). Have deep expertise in server-level systems (e.g., systems, containerization, Chef,

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 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

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, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Making data-driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide golden datasets and tooling to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively. In addition, Plaid will not be successful if we can't move quickly. We build the data systems and tools that enable everyone at Plaid to be data-driven, making analytics easy, obvious, and proactive across the company. Data Engineers heavily leverage SQL and Python to build data workflows that integrate with our Golang applications. We use tools like DBT, Airflow, Redshift, Atlan, and Retool to orchestrate data pipelines and define workflows. We work with engineers, product managers, business intelligence, data analysts, and many other teams to build Plaid's data strategy and a data-first mindset. You will be in a high impact role that will directly enable business leaders to make faster and more informed business judgements based on the datasets you build. You will have the opportunity to car

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. AI and intelligent systems are driving the fifth paradigm shift, following previous technological revolutions like mainframes, personal computers, the internet, and mobile devices. We believe, in the foreseeable future, AI will revolutionize the FinTech industry - from how consumers understand and manage their finances, to how developers build applications and how all companies operate. The fintech industry landscape will undergo a fundamental reshape. Plaid in the FinTech AI Ecosystem Plaid is uniquely positioned to become the financial data and insights backbone for AI applications and platforms in this evolving ecosystem. We believe consumers should be able to understand and manage their financial life through conversational AI interfaces using natural language. We believe consumers should have peace of mind with a trustworthy consent and authorization manager when agents shop for them. We believe identity verification and financial fraud prevention in AI-powered products should feel seamless and embedded for the end users. The list goes on. The most important AI companies, major fintechs, and customer agent platforms are actively trying to integrate Plaid into AI-powered products and solutions t

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. Making data-driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide golden datasets and tooling to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively. In addition, Plaid will not be successful if we can't move quickly. We build the data systems and tools that enable everyone at Plaid to be data-driven, making analytics easy, obvious, and proactive across the company. Data Engineers heavily leverage SQL and Python to build data workflows that integrate with our Golang applications. We use tools like DBT, Airflow, Redshift, Atlan, and Retool to orchestrate data pipelines and define workflows. We work with engineers, product managers, business intelligence, data analysts, and many other teams to build Plaid's data strategy and a data-first mindset. You will be in a high impact role that will directly enable business leaders to make faster and more informed business judgements based on the datasets you build. You will have the opportunity to carve out the ownershi

Datadog’s Cloud Networks team designs, builds, and maintains the production network infrastructure that powers everything built on top of our platform across AWS, GCP, Azure, and beyond. In this role, you’ll set technical direction for how we scale our multi-region, multi-cloud network footprint while keeping reliability and performance high. You’ll partner closely with internal teams and Cloud Service Providers to troubleshoot complex connectivity issues, integrate new networking capabilities, and improve the foundations our engineers and customers rely on. This is a high-impact opportunity to drive meaningful improvements in scale, resiliency, and cost efficiency. At Datadog, we place value in our office culture, the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Design, build, and operate cloud network infrastructure across AWS, GCP, Azure, and Neoclouds in a multi-region environment. Own connectivity between clouds, customers, and developers—ensuring scalable, secure, and reliable network paths. Set clear technical direction for expanding data centers and evolving the network while maintaining stability and performance. Improve cross-site and cross-region connectivity patterns to support Datadog’s growing platform needs. Lead deep investigations into latency, packet loss, and connectivity failures – from pcap and path analysis through to escalations with cloud providers that may originate from customer support Identify and deliver network-related efficiency and cost-saving opportunities that positively impact business health. Who You Are: You have deep networking expertise. You understand BGP, route policies, path selection, prefix advertisement, and what breaks in large-scale networking. You have substantial experience designing, building, and evolving large-scale Software-Defined Networks—inclu

About the Team The Workload team is responsible for designing and running OpenAI’s LLM training and inference infrastructure that powers frontier models at massive scale. Our systems unify how researchers train and serve models, abstracting away the complexity of performance, parallelism, and execution across vast GPU/accelerator fleets. By providing this foundation, the Workload team ensures that researchers can focus on advancing model capabilities while we handle the scale, efficiency, and reliability required to bring those models to life. About the Role We are looking for an engineer to design and implement the dataset infrastructure that powers OpenAI’s next-generation training stack. You will be responsible for building standardized dataset interfaces, scaling pipelines across thousands of GPUs, and proactively testing performance bottlenecks. In this role, you will collaborate closely with the multimodal researchers, and other infra groups to ensure datasets are unified, efficient, and easy to consume. In this role, you will: Design and maintain standardized dataset APIs, including for multimodal (MM) data that cannot fit in memory. Build proactive testing and scale validation pipelines for dataset loading at GPU scale. Collaborate with teammates to integrate datasets seamlessly into training and inference pipelines, ensuring smooth adoption and a great user experience. Document and maintain dataset interfaces so they are discoverable, consistent, and easy for other teams to adopt. Establish safeguards and validation systems to ensure datasets remain reproducible and unchanged once standardized. Debug and resolve performance bottlenecks in distributed dataset loading (e.g., straggler systems slowing global training). Provide visualization and inspection tools to surface errors, bugs, or bottlenecks in datasets. You might thrive in this role if you: Have strong engineering fundamentals with experience in distributed systems, data pipelines, or infrastructure.