System Engineer Cloudflare Hyperdrive Salary India in United States

We're transforming the grocery industry At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table. Instacart is a Flex First team There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview Instacart's Detection Engineering team sits at the core of our Security organization, building and operating the systems that identify, surface, and respond to threats across one of North America's largest grocery technology platforms. We own the full det

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Our web and API surfaces handle requests from guests and hosts alongside a growing volume of automated agents: AI assistants, crawlers, and scrapers. We build the systems that bring clarity to this traffic, combining in-house ML and vendor signals to decide in real time how to serve billions of daily requests. Anti-bot and anti-scraping detection is our most adversarial mandate, but the wider challenge is full traffic classification: building evaluation frameworks that tell legitimate automation apart from abusive actors, so high-stakes decisions hold up across the fleet. The Difference You Will Make: You will architect and maintain Airbnb’s end-to-end traffic classification ML systems, balancing high-performance model deployment with rigorous offline data pipelines. Success is measured by your ability to harden edge-traffic policies—targeting reduced bot-incident MTTM—and by establishing rigorous evaluation practices that ensure foundational signal accuracy and evasion-resistance across the fleet. A Typical Day: Own the complete lifecycle of traffic-scoring models, from problem framing to real-time deployment, managing the adversarial feedback loop to ensure high evasion-resistance and directly drive reductions in bot-incident MTTM. Architect robust offline-to-online pipelines that produce certified source-of-truth datasets, establishing rigorous evaluation frameworks—such as stratified benchmarks and leakage-prevention checks—to ensure every model improvement is empirically measurable and defensible. Execute model optimization within strict millisecond latency budgets at the

About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies — from the world's largest enterprises to the most ambitious startups — use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career. About the Organization Support Experience Engineering builds the technology that powers Stripe's global support operations. Our engineers create the systems and services that support agents worldwide rely on every day to resolve issues for Stripe's millions of merchants and users — at scale, in real time, and across a growing range of channels and offerings. The Case Resolution Platform team owns the core infrastructure and tooling that enables support agents around the world to do their best work. This includes case routing, live channels infrastructure spanning voice and messaging, machine translation, and integrations that connect Stripe's support stack to the platforms agents depend on. What you'll do We're looking for full-stack engineers who want to make an impact on the tools and infrastructure that power global customer support at scale. Our team collaborates with many cross-functional teams — from Infrastructure to Product to Operations — to deliver reliable, high-quality systems that support agents and merchants depend on every day. The team is actively expanding its live channels infrastructure to support new messaging platforms, including in Greater China, requiring collaboration with third-party providers in the region. Responsibilities Design, build, and maintain full-stack services and infrastructure — spanning both backend systems and user-facing tooling — that support agents around the world rely on to resolve merchant and user issues in re

About the Team OpenAI for Financial Services is part of OpenAI's Verticals organization, which focuses on accelerating the economy and knowledge work. We build AI products for financial institutions and the professionals who power them, from investment bankers and research analysts to investors and other financial services teams. We combine OpenAI's models, financial data, and enterprise knowledge to help professionals research companies, analyze markets, and produce high-quality work in the tools they use every day. We're a small, entrepreneurial team working closely with customers and partners across research, product, design, engineering, and go-to-market to bring new capabilities from idea to production. About the Role We're looking for backend engineers to build the systems that make advanced AI useful, reliable, and trustworthy in financial services. You'll build the data systems, agentic workflows, and enterprise integrations behind our products. You'll also help bring them into production at some of the world's largest financial institutions. This is a product-minded engineering role with significant ownership and zero-to-one building. You'll shape new products from the ground up, work directly with customers to understand their workflows, and collaborate across OpenAI to turn new model capabilities into products that professionals can trust with high-stakes work. In this role, you will: Design and build backend systems that power AI-native financial workflows across ChatGPT Work and Codex. Build infrastructure to ingest, index, retrieve, and serve financial data, company filings, market information, and firm-specific knowledge at scale. Develop integrations with financial data providers, enterprise knowledge systems, and customer environments, including the authentication, authorization, and entitlements required to use them securely. Build the systems that let models and agents use the right tools and data, preserve source provenance, and produce accurate,

About the Role The Mechanical Commissioning Project Engineer owns mechanical commissioning planning, readiness, quality coordination, and test execution oversight for the project. This role ensures mechanical systems are installed, inspected, started, balanced, controlled, and tested in a way that supports reliable integrated facility performance. Reports to the Commissioning Project Lead and partners closely with mechanical contractors, equipment vendors, design/engineering teams, the electrical commissioning lead, controls stakeholders, and vendor field/test engineers. Key Responsibilities Develop and maintain the mechanical commissioning scope, readiness criteria, inspection strategy, and discipline test execution plan. Review mechanical design packages, specifications, submittals, method statements, controls narratives, sequence assumptions, and testing requirements for commissionability and risk. Coordinate mechanical QA/QC inspections with contractors and vendor field/test engineers, including installation checks, pre-functional readiness, deficiency capture, and closeout tracking. Own mechanical commissioning procedure development and review, including equipment startup, functional testing, controls verification, balancing prerequisites, failure mode validation, and integrated systems testing inputs. Coordinate with equipment vendors on factory/site acceptance requirements, startup support, test prerequisites, documentation packages, and vendor participation during critical tests. Support readiness and execution for cooling, ventilation, hydronic, pumping, heat rejection, controls, and other project-specific mechanical systems. Lead discipline-level review of mechanical test results, deficiencies, corrective actions, retest requirements, trend logs, and acceptance evidence. Maintain mechanical commissioning dashboards and status inputs for the Commissioning Project Lead, including risk items, resource needs, test readiness, and issue aging. Partner with the E

About the Team OpenAI’s Application Engineering team builds the internal products and platforms that help OpenAI operate securely and at scale. We engineer, own, and evolve OpenAI’s core productivity ecosystem, creating secure applications, integrations, automation, and reusable tooling where off-the-shelf software is not enough. Our work spans employee-facing experiences and the services, APIs, control planes, and governance that make them reliable, permission-aware, and scalable. We also act as a customer zero for OpenAI’s technology, building the enterprise foundations that let employees and agents safely access the context, tools, and actions they need. We partner closely with IT, Security, product teams, and platform providers to turn company-wide problems into durable systems, learn from real internal workflows, and help shape the products we deploy. We create paved paths that let teams move quickly without compromising security or operational quality. About the Role As a Staff Software Engineer on Agent Productivity, you will shape the foundation that enables teams to build agents with secure access to the context and capabilities they need. Slack will be the first and deepest implementation surface—and where you spend most of your time—owning its application architecture, integrations, APIs, governance, and administration automation while building patterns that extend to internal systems, identity platforms, and other enterprise applications. This is a hands-on engineering role with broad organizational impact as agents support more employee workflows. You will define platform architecture, build reusable foundations, and establish secure patterns for identity, permissions, connectivity, and operations that make agents easier to develop, deploy, and manage. In this role, you will: Own the technical strategy and architecture that enable teams to build, connect, and deploy agents quickly and safely, using Slack as the primary implementation surface. Design and

About the Team We're building the foundation for a new kind of AI coworker: persistent agents that have their own environments, can meet people wherever they work, and continue making progress for as long as a task requires. Our goal is to help individuals, teams, and organizations delegate meaningful work to AI—not just ask questions or complete a single turn. Our work brings together product, agent, and infrastructure capabilities across OpenAI. Together, we are creating always-on virtual coworkers that can carry context across tasks, operate through the right tools and channels, and deliver reliable results in real workplace environments. About the Role We’re looking for a full stack product engineer to shape how people discover, direct, and collaborate with AI coworkers. You’ll own product experiences end to end, spending roughly equal time building intuitive frontend surfaces and the backend systems that make agentic workflows persistent, reliable, and useful. You’ll work at the intersection of product engineering, design, agent capabilities, and enterprise readiness—turning rapidly evolving model and platform capabilities into experiences customers can understand, trust, and use every day. In this role, you will: Design, build, and ship full stack product experiences that help people and teams delegate meaningful work to persistent AI coworkers. Create intuitive frontend interfaces for directing agents, reviewing their progress, understanding their actions, and collaborating on the work they produce. Build backend APIs, services, and data models that support persistent agent state, asynchronous execution, orchestration, and progress reporting. Develop workflows that help agents access relevant context, use tools, and collaborate with people across workplace surfaces and channels. Partner with product, design, research, and engineering teams to turn shared capabilities into cohesive products. Build trust into the product through clear user controls, understanda

About the Team The Legal team is building the next generation of AI-powered products and experiences for the legal industry. We are exploring how advanced AI systems can transform legal workflows, improve access to information, and enable legal professionals and organizations to work more effectively. As a founding member of the Legal engineering team, you will help define the technical foundation for this new product area from the earliest stages. You’ll operate at the intersection of AI, product, and real-world legal workflows—identifying opportunities, building prototypes, and turning emerging ideas into scalable products that can create meaningful impact. We operate with a startup-like mindset inside OpenAI: small teams, rapid iteration cycles, and a willingness to explore bold ideas, learn quickly, and adapt based on user feedback. Our goal is to build products that meaningfully improve how legal professionals work while leveraging OpenAI’s cutting-edge models and infrastructure. About the Role As a Founding Full-Stack Software Engineer on the Legal team, you will help imagine, build, and scale new AI-powered products for the legal industry. You’ll work across the stack to design intuitive user experiences, build robust backend systems, and create the foundations for products used by legal professionals and organizations around the world. You’ll have significant ownership from the earliest stages—working closely with product, design, research, and go-to-market partners to understand customer needs, shape product direction, and deliver high-impact solutions. This includes rapidly prototyping new concepts, building production-quality applications on top of OpenAI’s platforms, and developing new technical approaches when existing systems are not sufficient. We’re looking for engineers who thrive in ambiguity, have strong product instincts, and enjoy building from 0→1. You should be comfortable moving quickly, making thoughtful technical decisions, and taking owner

About the team The Agent Enablement AI Deployment Engineering (ADE) team works across engineering, product, design, partnerships, and strategic customers to grow an open ecosystem of agent-enabled sites and services. We help partners adopt the OpenAI tech stack related to identity, permissioning, agent-auth primitives so users can safely connect ChatGPT and Codex to the tools, services, and workflows they already use. Our team also works with external partners on defining the standards for agent access, marketplace offerings as well as other agent enablement initiatives to ensure users of ChatGPT and Codex go from intent to task completion seamlessly. About the role We are looking for an AI Deployment Engineer to help strategic partners design, build, validate, launch, and operate agent enablement integrations across web applications, connectors, APIs, CLIs, MCP servers, and developer tools. This is a hands-on, partner-facing product engineering role for someone who can contribute to the platform itself, lead sophisticated technical engagements, and turn ambiguous identity and agent-workflow requirements into secure, production-ready integrations. You will work across partner product and engineering teams and OpenAI’s product, engineering, design, partnerships, legal, policy, security, support, and go-to-market teams. You will identify high-value user journeys, choose the right integration path, prototype and review architectures, write code, run evaluations and dogfood, trace failures end to end, guide launch and rollout, and support post-launch iteration. The best person for this role moves fluidly between full-stack code, OAuth/OIDC and identity systems, product judgment, project leadership, and clear communication with engineers and executives. This role is a fit for a product-minded engineer who wants to stay close to users and partners while going deep on authentication, permissions, reliability, safety, and developer experience. The principle objective is to

About the Team Codex is OpenAI's software engineering agent. Codex Security extends that work into one of the most important product areas in AI: helping organizations find, validate, prioritize, and fix real vulnerabilities in the software they build and depend on. The Codex Cyber team is building the product and platform foundations for AI-native application security. This includes Codex Security product experiences, cloud-based security analysis, platform controls across Codex, customer deployment and support tooling, and infrastructure that helps security researchers and cyber models improve over time. The team is early, small, and growing quickly, with a mandate to move fast and hire exceptional builders. About the Role We are looking for software engineers first: strong full-stack or product-minded generalists who can own ambiguous product and platform problems end to end. Security experience is helpful, and security curiosity is important, but this is not a role for security specialists who only occasionally write code. The right person is an excellent builder who is excited to work in security and can turn complex research, product, and customer needs into reliable systems. You will work across user-facing product surfaces, developer workflows, backend services, security analysis pipelines, cloud infrastructure, and internal tooling. You may build features that make Codex Security more useful for application security teams, systems that scale cloud-based security analysis, platform controls that make agentic coding safer, or infrastructure that helps security researchers and models become more effective. You will collaborate closely with engineering, product, security research, infrastructure, and customer-facing partners as Codex Cyber becomes a major product and platform investment for OpenAI. In this role, you will: Build end-to-end product features for Codex Security, from developer-facing interfaces to APIs, backend services, and workflow tooling. Own a

About the Team OpenAI's research training infrastructure powers how our frontier models are trained and evaluated. The Simulation team sits at the intersection between the agentic harness that powers OpenAI's products and the research infrastructure where GPT-next is trained, ensuring that our model's training environment is as realistic as possible. This team owns the integration layer that connects our production harness capabilities into the training stack. The work is highly cross-functional and high leverage: researchers depend on it to run experiments and evaluations reliably as well as to develop the next generation of harness capabilities. Failures in this surface can materially affect training velocity and correctness. About the Role We're looking for a Principal Software Engineer to lead the architecture and evolution of the Simulation Platform. You'll own a critical interface between research and engineering, building the systems, APIs, and operational patterns that let researchers use agentic coding infrastructure safely and effectively in training environments. This role is ideal for a senior backend or infrastructure engineer with strong technical judgment, product sense for highly technical users, and the ability to drive execution across multiple teams. The highest-leverage work is building robust infrastructure that supports and accelerates research without compromising engineering quality. In this role, you will Design, build, and evolve the integration between the Codex harness that powers OpenAI's products and research training infrastructure used for training GPT-next Build a platform for our LLMs to train and be evaluated in simulated environments that mimic their deployment setting as closely as possible, on every axis: agentic harness, compute substrate, timing, tools, data sources, humans in the loop, and more Own major integration surfaces end-to-end, from architecture and API design through rollout, operations, and long-term maintenance Bu

About the Team OpenAI’s Infrastructure Operations team is responsible for the availability, reliability, and operational excellence of one of the world’s largest AI infrastructure networks. The team owns day-to-day operations of production AI networks across Industrial Compute's data centers, working with colocation providers, deployment teams, and hardware vendors to deliver highly available GPU infrastructure for AI training and inference workloads. About the Role We are seeking an Infrastructure Operations Engineer to operate and improve the large-scale Ethernet fabrics that support GPU clusters, storage systems, and management infrastructure. This role combines hands-on production operations with automation, observability, and incident response across a global AI network. The ideal candidate has experience operating high-availability data center, cloud, AI, or HPC networks and can move comfortably from physical-layer troubleshooting to routing and fabric behavior, change execution, and root-cause analysis. You will partner closely with network architecture, systems engineering, GPU engineering, storage engineering, security, deployment, site operations, service providers, colocation partners, and hardware vendors to raise reliability and reduce operational toil. Key Responsibilities Own the operational health, availability, and reliability of production AI network infrastructure across Industrial Compute's data centers. Monitor, troubleshoot, and resolve network incidents while meeting service-level objectives (SLOs), reducing Mean Time to Detect (MTTD), and minimizing Mean Time to Recovery (MTTR). Operate and maintain large-scale Ethernet fabrics supporting GPU compute, storage, and management networks. Execute production network changes, maintenance windows, and capacity expansions with minimal customer impact. Manage the hardware lifecycle, including switch and optics replacements, RMA coordination, software upgrades, and preventive maintenance. Support new A

About the Team The Personality & Model Behavior team, within OpenAI’s broader Personal AGI team conducts research on how to shape personalities and guide the behavior of models. We think about topics such as emotional intelligence, reasoning, and how models interact thoughtfully with users. We’re particularly interested in understanding how individual users want ChatGPT to behave, and creating personalized models that feel uniquely tailored to each user. We integrate this research into ChatGPT and other OpenAI products that are used by hundreds of millions of users. About the Role We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models, and in areas like reinforcement learning and reward modeling. An ideal candidate is passionate about product-driven research. In this role, you will: Conduct research around personalization, personality, and model behavior by leveraging and developing tools such as synthetic data, reward modeling, and reinforcement learning. Build robust evaluations and model training pipelines to facilitate our research. Innovate new post-training methods. 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. You might thrive in this role if you: Have a deep understanding of machine learning and its applications. Have prior knowledge in training and optimizing models and building evaluations. Are willing to dive into large ML codebases to debug issues. Thrive in dynamic and technically complex environments. Have a track record of delivering innovative, out-of-the-box solutions to address real-world constraints. 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 o

About the Team We bring OpenAI's technology to the world through products like ChatGPT and the OpenAI API. We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth. About the Role We are looking for an experienced Research Engineer to work on retrieval & search problems across our API and ChatGPT. As the AI landscape has evolved over the last few years, retrieval & search have emerged as key use cases for our models, and we are investing in ensuring that we can offer these search-based product experiences for our users. You will be at the center of our retrieval & search efforts as a company, and the progress you drive here will reach millions of end users. In this role, you will: Work on retrieval & search algorithms and methodologies in close collaboration with our research team, including problems in such domains as document search, enterprise search, knowledge retrieval, and web-scale search. Deploy these search methodologies into production in both the API and ChatGPT to be used by millions of end users. Explore novel research topics in retrieval & search that may inform our product strategy in the medium and long term. Partner with researchers, engineers, product managers, and designers to bring new features and research capabilities to the world You might thrive in this role if you: Have extensive prior experience building and maintaining production machine learning systems. Have prior experience working with vector databases, search indices, or other data stores for search and retrieval use cases Have prior experience building and iterating on internet-scale search systems Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done Have the ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or de

About the Team The Post-Training Frontiers team is responsible for training the frontier agents OpenAI ships to the world (GPT-Next). We train the flagship agentic models behind Codex, ChatGPT, and the API through large-scale reinforcement learning. The team’s work spans four areas. First, execution and science: working with teams across OpenAI to decide what can go into the final model and how, using scientific experiments and evals that are representative of the final pipeline so issues can be recognized early. Second, RL scaling: executing the final large-scale reinforcement learning run, making sure GPUs are used efficiently and training stays healthy. Third, research: improving horizontal capabilities like instruction following, factuality, memory, and multi-agent behavior, where the team’s broad visibility helps identify cross-cutting improvements across teams and domains. Fourth, engineering: maintaining the infrastructure stack and internal tools to ensure that both the final run and all integrations go as smoothly as possible and that the systems are easy to work with. About the Role This role focuses on keeping our frontier RL training runs fast, reliable, and unblocked. You will work across engineering and infrastructure problems as they emerge, from scaling and orchestration issues to inference bottlenecks, numerical problems, and hardware failures, as well as supporting large horizontal integrations in the big run, like multi-agent capabilities or memory. This is a role for a strong generalist who quickly learns anything needed for the task, has high attention to detail, debugs deeply, and is motivated by fixing the highest-impact problem in front of the team. In this role, you will: Keep large-scale async RL training runs moving by jumping into the most urgent engineering and infrastructure problems. Debug issues across training systems, inference, orchestration, scaling, and distributed infrastructure. Improve the reliability and efficiency of RL trai