Staff Infrastructure Engineer in United States

About the Team The Enterprise Identity team builds the identity foundation that enables organizations to adopt and use OpenAI products securely and reliably. The team owns the enterprise identity stack, including SSO, SCIM, tenant architecture, and identity capabilities across the enterprise admin experience and OpenAI's growing multi-product portfolio. About the Role We are looking for a hands-on senior technical leader to own the architecture and evolution of OpenAI's Enterprise Identity systems. You will set the long-term technical vision for the entire stack, establish shared identity primitives across products, and be accountable for systems that are foundational to our enterprise business. This role requires operating well beyond a single service or feature area. You will identify the most consequential architectural investments, align teams around durable solutions, and ensure our identity platform meets an exceptionally high bar for scale, availability, latency, and security. This role will be based in our San Francisco or Mountain View office. In this role, you will: Own the technical vision and architecture for the Enterprise Identity stack, including SSO, SCIM, tenant architecture, groups, permissions, and identity capabilities in enterprise administration surfaces. Lead the design and evolution of highly available, latency-sensitive identity systems serving a large and diverse global enterprise customer base. Establish common identity models and primitives that work consistently across OpenAI's products and enable the organization to scale. Set a high security bar by anticipating abuse cases, failure modes, and the long-term implications of new capabilities. Drive alignment across enterprise product, infrastructure, and security partners, resolving ambiguity and influencing roadmaps beyond the immediate team. Provide technical leadership to senior engineers and raise the quality of architecture and execution across the broader organization. You might thr

About the Team OpenAI, in partnership with our capital and technology partners, is building a global network of advanced datacenters to support the most demanding AI workloads. The Industrial Compute team ensures that all datacenter systems are manufactured, delivered, and commissioned to the highest standards of quality, reliability, and performance. We work closely with manufacturing partners, engineering teams, and operations staff to ensure that every component is delivered ready for installation, startup, and long-term service. About the Role We are seeking an experienced Quality Engineer (QE) to drive Product and Site Quality initiatives across OpenAI’s infrastructure ecosystem. In this role, you will establish, implement, and manage a comprehensive, quality-focused program across our global supply chain network, ensuring excellence from design through deployment. You will be responsible for end-to-end quality of finished products, as well as maintaining and elevating manufacturing site quality standards. Working cross-functionally with Design (NPI) and Engineering teams, you will help achieve First Pass Yield (FPY), quality, and reliability targets. This includes leading site and fixture validation efforts, driving yield improvement initiatives (Yield Bridge, CPI), and implementing robust corrective and preventive actions (CAPA) to resolve issues at their root cause. In addition, you will play a key role in supplier quality management, assessing and qualifying new vendors, overseeing ongoing supplier performance, and ensuring readiness for future business awards. You will lead vendor audits, monitor key performance metrics, and coordinate corrective actions to ensure predictable delivery schedules, reduced operational risk, and high system reliability. By partnering closely with external suppliers and internal Engineering and Operations stakeholders, you will help ensure OpenAI’s datacenter infrastructure is delivered on time, meets the highest quality standa

The Team Platform Engineering is the department within SRE that is responsible for a range of critical infrastructure and operational functions that support the broader engineering organization. Among these are our multi-cloud-provider Kubernetes infrastructure, deployment machinery, and observability and alerting systems. The Fabric team manages the infrastructure that enables secure communication between systems and from the public internet. Their responsibilities encompass network architecture, service mesh, and edge load balancing, ensuring customer data remains safe in transit. The team plays a crucial role in developing and maintaining the reliable and globally connected multi-cloud network that supports MongoDB products. This role can sit in our NYC HQ, our smaller Austin, Palo Alto, or San Francisco offices, or fully remote from anywhere in North America. When based in an office, we provide hybrid work accommodation. Role Overview We are seeking a talented Site Reliability Engineer (SRE) with a strong networking background to join the Fabric team. This role is pivotal in building and maintaining the robust infrastructure necessary for secure and efficient communication between our services. As an SRE on the Fabric team, you will leverage your expertise in networking, distributed systems, and automation to ensure our systems are resilient, scalable, and reliable. The ideal candidate should Have 10+ years of experience working on software and operating distributed systems, with deep expertise in networking fundamentals and a good understanding of how the internet works, e.g. TCP/IP (including IPv6), DNS, TLS/mTLS, BGP, tunnels, overlays, and SDN principles Possess a customer-focused mindset, driving improvements that benefit end-users Value efficiency in processes and operations, and display a strong preference for automation over manual processes (“allergic to ops work”) Be intimately familiar with modern cloud-based infrastructure and the network design prim

About the Team ChatGPT is a rapidly evolving system: new capabilities ship continuously, product surfaces change quickly, and usage patterns shift week-to-week. Supporting that pace requires infrastructure that can handle real production constraints—high concurrency, unpredictable traffic patterns, complex dependency graphs, and frequent change. The ChatGPT Infrastructure team builds and operates the platforms that enable fast iteration without compromising performance or reliability. We design shared systems, data paths, rollout mechanisms, and reliability guardrails that teams rely on to ship changes to ChatGPT at scale. We focus on high-leverage infrastructure: primitives and “golden paths” that incorporate operational lessons as defaults, so engineers don’t need to rediscover failure modes, latency pitfalls, or integration issues each time they build something new. About the Role We’re hiring Senior and Staff Engineers to design and build infrastructure systems that underlie ChatGPT and multiply the effectiveness of teams building user experiences. This is not a support-only role. It’s a platform-building role: you’ll define interfaces, develop core abstractions, and create tooling to make safe, fast iteration the norm. Your work will reduce friction, prevent regressions, improve performance, and ensure systems scale gracefully as the product grows. Where You Can Have Impact You might work on one or more of the following areas (without being restricted to any single area): Platform foundations & frameworks: Core libraries, service frameworks, and shared components that standardize system building, integration, and evolution. Scalability & performance primitives: Patterns and infrastructure that reduce tail latency, improve throughput, and keep costs predictable as demand increases. Reliability guardrails: Mechanisms that prevent outages by design—rate limiting, load shedding, dependency isolation, backpressure, safe fallbacks, and robust regression contr

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . The Data Product Platform is mission-critical to accelerating data-driven decision-making at Pinterest on the foundation of 100s of thousands of tables and an exadata-scale data warehouse. We strive to provide effortless, efficient, and reliable data products and platforms that power the entire company. We achieve this by investing in three core areas: Data Warehouse: Building and managing the foundational data warehouses that enable key analyses across both our core engagement and monetization products. Analytical Velocity: Creating powerful analytical tools that empower internal data users to leverage our vast data assets and capable infrastructure effectively. Data Governance: Defining and implementing the data governance policies and tools necessary to ensure the responsible, efficient and compliant storage and handling of all data. We are s

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why this role? Are you energized by leading the design of high-performance, scalable and reliable machine learning systems? Do you want to set technical direction and help shape the next generation of AI platforms powering advanced NLP applications? We are looking for a Lead Member of Technical Staff to join the Model Serving team at Cohere. The team is responsible for developing, deploying, and operating the AI platform delivering Cohere's large language models through easy to use API endpoints. In this role, you will provide technical leadership across multiple teams, driving the architecture and strategy for deploying optimized NLP models to production in low latency, high throughput, and high availability environments. You will serve as a key point of contact for customers, leading the design of customized deployments to meet their specific needs, and mentoring engineers to raise the technical bar across the team. You may be a good fit if you have: 8+ years of engineering experience running production infrastructure at a large scale, with a track record of technical leadership Demonstrated experience leading the architecture

Who Are We? Postman is the world’s leading API platform, used by more than 45 million+ developers and 500,000 organizations, including 98% of the Fortune 500. Postman is helping developers and professionals across the globe build the API-first world by simplifying each step of the API lifecycle and streamlining collaboration—enabling users to create better APIs, faster. The company is headquartered in San Francisco and has offices in Boston, New York, Austin, Tokyo, London, and Bangalore - where Postman was founded. Postman is privately held, with funding from Battery Ventures, BOND, Coatue, CRV, Insight Partners, and Nexus Venture Partners. Learn more at postman.com or connect with Postman on X via @getpostman. P.S: We highly recommend reading The "API-First World" graphic novel to understand the bigger picture and our vision at Postman. The Opportunity Postman is seeking an experienced AI Systems Reliability Engineer to help define, build, and maintain the infrastructure and processes that ensure the reliability, scalability, and performance of Postman’s AI-powered API and agentic systems in production. This role focuses on monitoring, availability, incident response, and automation to support AI services and tools trusted by millions of developers globally. What You’ll Do Develop and manage reliability metrics (SLOs) for AI-driven API services and agentic AI platform features Implement comprehensive observability and monitoring systems for real-time performance and fault detection Design and drive automated failover, recovery, and incident response strategies for high-availability AI infrastructure Optimize resource utilization, particularly GPU/accelerator efficiency, ensuring cost-effective AI system operation Collaborate closely with engineering, platform, and product teams to align reliability efforts with broader organizational goals Lead efforts to build internal tooling and automation focused on AI system stability and operational excellence Drive continuo

Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange™️ platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 160+ public exchanges globally and thousands of private exchanges at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform. We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler. Role We are looking for a Sr. Staff Software Development Engineer-AI Security to join our team. This is a Hybrid (based in San Jose, CA or Bellevue, WA with a 3 days in office requirement) role, reporting to the Director of Software Engineering in the Emerging Tech department. You will be responsible for designing and implementing core infrastructure components and distributed systems, serving as a foundational architect for our AI security solution. This high-impact role focuses on scaling security infrastructure to support hundreds of millions of users, collaborating with stakeholders across the development lifecycle to drive innovation and technical excellence. What you’ll do (Role Expectations) Architect, develop, and optimize a low-latency, high-throughput AI Security plane utilizing Rust, specifically leveraging its async/await model for highly efficient I/O and service-oriented architecture Build resi

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Specifically, you'll be working on Modal's machines layer: the fleet of bare metal and cloud hosts that every Function, Sandbox, and training job runs on, and the control plane that provisions, images, monitors, and repairs them. You'll automate the integration of new capacity from a growing set of hardware providers; from auditing and benchmarking hosts and clusters, to maintaining our machine images, configuring GPUs, RDMA, networking, and storage, and getting machines into production. You'll build the automation that keeps the fleet healthy without human intervention: detecting bad GPUs, thermals, and disks. You'll dig into whatever is between the hardware and the software that runs on

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Specifically, you'll be working on the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: hundreds of petabytes of data, replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter. You'll make cold starts feel local when the data is hundreds of milliseconds away, designing the caching, preloading, and peer-to-peer layers that hide object-store latency and keep public ingress off saturated uplinks. You'll own durability and cost at petabyte scale, from streaming and batch replication between origins, to garbage collecti

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for a strong technical lead to guide the engineers designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. You'll lead the team responsible for Modal's machines layer: the fleet of bare metal and cloud hosts that every Function, Sandbox, and training job runs on, and the control plane that provisions, images, monitors, and repairs them. You'll own the full lifecycle of a machine, from accepting and benchmarking new hardware from a growing set of providers, to network bring-up, kernel and image management, GPU and disk health tracking, and automated remediation of unhealthy hosts. You'll manage a team of 3–8 engineers while staying hands-on across the stack which involves BMCs, firmware, PXE, bootloaders, Linux networking, drivers, and distributed control-plane services, and you'll shape our long-

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for a strong technical lead to guide the engineers designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. You'll lead the team responsible for the distributed object storage system that underpins every container image, volume, and checkpoint on Modal: hundreds of petabytes of data, replicated across multiple cloud object stores and a CDN, cached on local NVMe across a large fleet of workers in many datacenters, and shared peer-to-peer within each datacenter. You'll set technical direction for the primitives that other teams (filesystems, training, sandboxes) build on, balancing durability, latency, throughput, and cost. You'll own the roadmap from today's hardest problems (garbage collection at petabyte scale, active-active replication, rate limiting that protects the upstream without wasting ut

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our serverless platform. Requirements: 5+ years of experience writing high-quality production code Experience building high-performance distributed systems at a large scale (the more battle scars, the better) Strong cloud skills Strong knowledge of low-level operating system foundations (Linux kernel, file systems, containers, etc.) Experience with performance engineering (tell us a story of when you shaved off a few milliseconds!) Ability to work in-person in our NYC or SF office. Prior experience with Rust is nice to have, but not required. Ability to participate in on-call rotation and respond to production incidents.

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience in making ML systems performant at scale. If you are interested in contributing to open-source projects and Modal’s container runtime to push language and diffusion models towards higher throughput and lower latency, we’d love to hear from you! Requirements: 5+ years of experience writing high-quality, high-performance code. Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT). Familiarity with Nvidia GPU architecture and CUDA. Experience with ML performance engineering (tell us a story about boosting GPU performance — debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc). Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We're looking for strong backend engineers who love building a developer tools used by the largest AI companies in the world. You’ll be building for things at scale, but also for new AI workflows that change every day. Requirements: Experience building and shipping modern web applications end-to-end. We care more about what you’ve built than how many years you’ve been building. Comfort working across the stack: TypeScript on the frontend, Python services on the backend, and ClickHouse for data and analytics. Deep knowledge of observability tools and patterns used for large-scale workloads such as custom sandboxes, training and inference for large language (LLM) and diffusion models. Experience with at least one of: billing/payments systems, B2B SaaS tooling, or enterprise software, or LLM / diffusion models inference and training loads. Strong product instincts; yo