Cloud Operations System Administrator in United States

Datadog’s Implementation Services team helps customers implement and deploy Datadog quickly. Our team of architects leads the discovery, design, build, and launch of the Datadog platform to help customers accelerate time to value and get the most out of their investment. As a member of our team, you will be responsible for developing the technical roadmap for a customer’s implementation, and leading them through it every step of the way. 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 and guide execution of Datadog implementations, including gathering requirements, building technical architecture, and supporting deployment and launch Guide customer onboarding through co-development working sessions and assist when they run into roadblocks Write automation and design architecture for deployment templates Manage the operation of implementation projects Collaborate with other teams, including Customer Success, Technical Account Management, and Sales to deliver an exceptional onboarding experience Who You Are: Experience designing and delivering technical solutions for DevOps Monitoring or architecture systems Hands-on experience with cloud platforms (AWS, Azure, GCP) and compute technologies (EC2, serverless, VMware, VMs, Docker, Kubernetes). Experience with Config Management, IAC and CI/CD tools, including Ansible, Puppet, Terraform, Chef, Jenkins, Circle CI, GitHub Actions, Azure Pipelines, etc… Experience programming/scripting with any of the following: Java, Python, Ruby, Go, Node.JS, PHP, and .NET etc Background in security operations, including SOC management, SIEM tooling (e.g. Splunk Professional Services), and designing or supporting zero-trust networking architectures Successful track record with 5+ years exper

About the Team Security is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity. The Security team protects OpenAI’s technology, people, and products. We are technical in what we build but are operational in how we do our work, and are committed to supporting all products and research at OpenAI. Our Security team tenets include: prioritizing for impact, enabling researchers, preparing for future transformative technologies, and engaging a robust security culture. About the Role As a Security Engineer you will join our OpenAI engineers and researchers in building, operating and securing transformational AI technologies. This role will focus on all aspects of Detection & Response but with a strong emphasis on detecting insider threats and influencing controls to safeguard OpenAI's most sensitive assets. In this role, you will: In this role, you will: Innovate on Detection and Response infrastructure to engineer and automate end-to-end detection and investigation workflows. Develop, measure, and tune detection rules to ensure effective and sustainable operations. Drive projects across OpenAI’s technology stack with a focus on insider threats, ranging from access abuse and intellectual property theft to novel risks emerging within AI infrastructure. Partner closely with cross-functional stakeholders, including HR, Legal, and peer investigative teams, providing technical expertise and evidence to support investigations. Collaborate on cutting-edge AI research, and use AI to improve OpenAI’s Security posture. You might thrive in this role if you: 5+ years experience working in a detection/response or insider-risk role.. We are seeking mid-level and senior candidates. You have broad familiarity with operating systems and platforms such as macOS, Windows, Linux, and Kubernetes, along with experience in cloud infrastructure. Knowledge of modern adversary tactics and attack paths, data exfiltration techniques, and h

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake's Data Engineering organization builds the platform that ingests, transforms, and stores data for modern lakehouse architectures — powering billions of queries, DML, and DDL operations with industry-leading price-performance. We lead the industry's shift to open data lakes through our work on Iceberg and Polaris, and we deliver capabilities like Snowpark, Dynamic Tables, cross-region replication, time travel, and zero-copy cloning at enterprise scale. We are investing in a new line of applied research — building toward verified data infrastructure and trustworthy data systems — that brings formal methods, automated reasoning, and modern AI techniques to bear on the hardest problems in our distributed systems and developer tooling. The goal is to improve correctness, reliability, and engineering velocity at a scale very few platforms operate at. We're hiring at both the Staff and Principal level; we'll calibrate the offer to the candidate's experience and scope of impact. What you'll do Lead research projects that apply formal methods, program analysis, automated reasoning, and AI-driven techniques (including code generation and modeling) to real problems in our cloud data platform. Translate research ideas into prototypes, then into shipped capabilities that move

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).

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 At Postman, we are revolutionizing the way developers build, trace, and automate API workflows with Postman Flows , a powerful visual programming tool designed to simplify the development and sharing of API-powered applications. With an intuitive drag-and-drop interface, Postman Flows enables teams to collaborate and showcase their APIs regardless of technical expertise. We are looking for a Software and Systems Engineer to help scale and maintain the Flows runtime system. This system runs mission-critical automations in the cloud, with a focus on low latency, high throughput, and high availability. You’ll play a vital role in developing, deploying, and operating our backend services and infrastructure in a Kubernetes-based cloud environment. We’re looking for an experienced engineer who is excited not only about hands-on building as we ship and iterate on a weekly basis to get our product ready for GA, but who can also serve as a role model and mentor to other engineers. This role involves making key technical decisions and improvements to the system, as well as effectively making impact through influence wit

About the Team The Consumer Devices team at OpenAI builds end-to-end hardware and software systems that bring AI into the physical world. We work at the intersection of custom silicon, embedded systems, operating systems, and cloud services to deliver reliable, production-ready devices at scale. About the role We are looking for an Operating Systems Engineer to build and harden the OS foundations for OpenAI products. We are especially interested in experienced, passionate, and innovative operating systems developers who thrive on building foundational platform software and solving hard problems in security, privacy, performance, power, and reliability. You will work across the OS kernel, core OS services, security and privacy primitives, performance and power, and the frameworks that connect applications and UI to the system. This role emphasizes deep debugging and systems ownership from development through production. You will collaborate closely with embedded, firmware, hardware, application, and product engineering teams. Experience with hardware bring-up is a plus, but not required. What you will do Work on end-to-end OS capabilities spanning the OS kernel, userspace services, application frameworks, UI toolkits, and application-facing APIs. Develop, integrate, and maintain OS components, both kernel-bound and in userspace, including scheduling, memory management, filesystems, drivers, IPC/RPC mechanisms, and security-relevant subsystems. Build and maintain core OS services and daemons (init, service management, device discovery, networking primitives, time, logging, update hooks, crash handling, and so on). Design and implement security and privacy mechanisms: Secure boot and measured boot integration points (where applicable). Mandatory access control and sandboxing. Secrets management, secure storage, key handling, and least-privilege service design. Privacy-preserving telemetry, data minimization, and user-consent oriented system behaviors. Establish a perfo

We are looking for a Senior System Software Engineer, Software Defined Networking to design, build, and operate highly performant and scalable SDN solutions for NVIDIA's AI Clouds hosting GPU-accelerated workloads — including hyperscale multi-node training, inference, cloud gaming, and cloud functions. This role spans the full lifecycle of our SDN stack — from designing and developing new control and data plane software to ensuring operational excellence in production through reliability engineering, CI/CD, observability, and incident response. What you'll be doing: Design and develop next-generation multi-tenant cloud SDN control and data plane software (OVS, OVN, OpenFlow) Build Infrastructure-as-a-Service virtual network orchestration and services using gRPC and REST to support tenant workload security and performance SLAs for BMaaS, VMaaS, and Kubernetes Drive upstream contributions to OVN-Kubernetes and related open-source projects Develop software for network observability — monitoring, telemetry, intelligent metering, and performance analysis Operate and support OVS-OVN based SDN solutions in large-scale NVIDIA AI Cloud environments Own end-to-end observability for the SDN stack — build and maintain monitoring, alerting, distributed tracing, and dashboarding to ensure real-time insight into network health, performance, and tenant SLAs Design, enhance, and maintain CI/CD pipelines (GitLab) across Linux host networking, OVS, OVN, and Kubernetes CNIs Implement GitOps approaches or related experience for secure, seamless integration with cloud infrastructure Drive reliability through incident management, resource monitoring, and performance tuning<

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE As a Cloud Platform Engineer, you'll envision and build robust systems and processes that ensure our infrastructure is scalable, reliable, and efficient. This can range from automating deployments and monitoring systems to optimizing performance and managing incidents. We all work closely with our users, learning from their past struggles in operationalizing ML, onboarding them onto our platform, and turning our learnings into ideas for improving Baseten. EXAMPLE INITIATIVES You'll get to work on these types of projects as part of our Infrastructure team: Multi-cloud capacity management Inference on B200 GPUs Multi-node inference Fractional H100 GPUs for efficient model serving RESPONSIBILITIES Build and maintain scalable infrastructure to support the deployment and operation of machine learning models. Establish standards and best practices for reliability and performance across the infrastructure. Automate processes when relevant, particularly for managing CI/CD pipelines. Own products and projects end-to-end, functioning as both an engineer and a project manager, with a focus on user empathy, project specification, and end-to-end execution. Collaborate with cross-functional teams to understand project requirements and translate them into technical solutions. Mentor junior team members and contribute to knowledge sharing within the organization. Navigate ambiguity and exercise good judgment on tradeoffs and

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. Position Summary We are seeking an accomplished Principal Cloud Storage Engineer to lead the design, engineering, and evolution of our private cloud storage platforms. This role will focus on large-scale storage architecture, data protection, cyber recovery, and resiliency technologies across complex enterprise environments. The ideal candidate will combine deep technical expertise in storage systems with strong leadership, architectural vision, and the ability to influence technical direction across the organization. Key Responsibilities Architect and engineer enterprise storage platforms that ensure data integrity, availability, security, and disaster recovery readiness Design and implement end-to-end storage solutions, including Software Defined Storage, SAN, NAS, and object storage across private cloud and data center environments Drive strategic technology decisions by evaluating emerging products, tools, and standards supporting storage, data protection, cloud, and compute platforms Lead infrastructure initiatives involving storage modernization, data protection, cyber recovery, data migration, and resilience engineering Develop and execute enterprise strategies for backup, recovery, cyber vaulting, and business continuity Create and maintain comprehensive documentation of storage architectures, configurations, policies, and operation

About the Team OpenAI Consumer Devices is building the next generation of products that bring powerful AI into people’s everyday lives. Guided by OpenAI’s mission to ensure AGI benefits all of humanity, our team combines world-class researchers, engineers, designers, and operators who care deeply about creating useful, intuitive, and responsible technology. You’ll have the opportunity to work alongside exceptional people on ambitious, zero-to-one challenges at the intersection of hardware, software, and AI. This is a chance to help define an entirely new category of products—and shape how people experience AI in the future. Our team works across silicon, embedded systems, operating systems, and cloud services to build reliable consumer devices and the novel platforms required to support them. We partner closely with research to bring advanced AI capabilities into the physical world. About the Role As an Operating Systems Engineer focused on on-device inference, you will design, develop, and ship the OS stack that makes advanced AI capabilities reliable, responsive, and energy efficient on consumer devices. Your work will span OS services and frameworks, inference runtime integration, model fitting, scheduling, and performance and power management. You’ll partner with research to adapt models to device constraints, make design decisions across the stack, and carry solutions from early exploration through integration and production. In this role, you will: Build the inference platform: Design and implement maintainable OS services, frameworks, and clear interfaces for inference execution, model loading and lifecycle, and resource management. Fit models to device constraints: Partner with researchers on quantization, runtime integration, and memory optimization to meet memory, compute, and energy budgets while evaluating model quality and product behavior. Coordinate system resources: Develop scheduling and resource policies that balance inference with other device act

About the Team OpenAI Consumer Devices is building the next generation of products that bring powerful AI into people’s everyday lives. Guided by OpenAI’s mission to ensure AGI benefits all of humanity, our team combines world-class researchers, engineers, designers, and operators who care deeply about creating useful, intuitive, and responsible technology. You’ll have the opportunity to work alongside exceptional people on ambitious, zero-to-one challenges at the intersection of hardware, software, and AI. This is a chance to help define an entirely new category of products—and shape how people experience AI in the future. Our team works across custom silicon, embedded systems, operating systems, and cloud services to build reliable consumer devices and the platforms behind them. We connect kernel development with the broader software stack to deliver complete product capabilities. About the Role As an Operating Systems Engineer focused on the Linux kernel, you will design, develop, and maintain the kernel capabilities that underpin OpenAI’s consumer devices. You’ll bring deep expertise in one or more Linux kernel subsystems and carry solutions through the higher-level software stack. Your ownership will extend into the userspace services, libraries, tools, and interfaces needed to deliver complete product features. You’ll shape the boundaries between kernel and userspace, make design decisions across the stack, and see your work through development, integration, and production. In this role, you will: Build kernel capabilities: Design, implement, and maintain Linux kernel subsystem changes that support device capabilities and product requirements. Own features across the stack: Choose appropriate kernel and userspace boundaries, and build the interfaces and supporting components needed to deliver reliable features in shipped products. Debug complex system behavior: Use tracing, profiling, instrumentation, and diagnostic tools to resolve correctness, concurrency, p

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. Snowflake runs large scale cloud infrastructure to deliver its own service — production and internal deployments, Kubernetes fleets, CI/CD, etc. Our cloud spend is in billions of dollars per year. The Cloud Efficiency team builds a unified, self-serve cloud efficiency platform along with AI skills and agents that makes spend observable, attributable, governable while driving recommendations and optimization of our cloud spend. AS A SOFTWARE ENGINEER AT SNOWFLAKE YOU WILL: Design, develop, and maintain scalable platform for resource ownership registry, usage attribution, utilization measurement, and cost modeling. Build AI agents, tools and automation to enhance system monitoring, alerting, and root cause analysis. Improve and optimize data ingestion, storage, and query efficiency for cloud utilization, cost and efficiency data at scale. Collaborate with teams across Snowflake to understand attribution and observability needs and implement solutions that improve operational visibility. Contribute to open-source and industry best practices in monitoring and distributed systems monitoring. Ensure high availability, reliability, and performance of team-managed platforms by participating in on-call rotations and incident management. Partner with Finance, Product and Engineering

About the Team The Codex team is responsible for building state-of-the-art AI systems that can write code, reason about software, and act as intelligent agents for developers and non-developers alike. Our mission is to push the frontier of code generation and agentic reasoning, and deploy these capabilities in real-world products such as ChatGPT and the API, as well as in next-generation tools specifically designed for agentic coding. We operate across research, engineering, product, and infrastructure—owning the full lifecycle of experimentation, deployment, and iteration on novel coding capabilities. About the Role As a Performance & Systems Engineer on the Codex team, you will be responsible for whole-system optimization across a complex, evolving stack. Codex spans LLM inference, cloud orchestration, agentic work management, and multiple product surfaces. Your job will be to identify and land high-leverage changes—across infrastructure, modeling, and product layers—that make Codex agents significantly faster and cheaper to serve. We’re looking for generalists who thrive in ambiguity and love chasing performance bottlenecks to ground. This is a high-ownership role where your work will directly improve the experience of millions of users. 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: Hunt down and address inefficiencies across the Codex system stack, from agent behavior to LLM inference to container orchestration, and beyond. Build tooling to measure, profile, and optimize system performance at scale. Collaborate with researchers and engineers to land high-ROI changes that improve latency and cost. You might thrive in this role if you: Have experience operating across both ML systems and cloud infrastructure. Enjoy diving into messy, ambiguous problems and emerging with clear wins. Think holistically about performance, balancing spee

About the Team The Hardware Health and Observability team owns the end-to-end health lifecycle of OpenAI’s global compute fleet. Our mission is to maximize healthy, usable compute across accelerator vendors, generations, cloud providers, and regions through reliable health signals, automated remediation, and scalable operational tooling. We build the systems that observe, detect, remediate, and verify hardware issues across GPUs, CPUs, networking, and platform infrastructure, enabling frontier model training and inference workloads to run reliably at hyperscale. We are the last line of defense for the success of OAI’s production and research workloads. About the Role On the Hardware Health and Observability team, you’ll build critical infrastructure that keeps OpenAI’s largest compute clusters healthy and operational at scale. Even small numbers of unhealthy systems can impact large-scale training and inference workloads. This team focuses on minimizing downtime, improving fleet efficiency, and ensuring compute resources remain continuously available to researchers and product teams. Engineers on this team own problems end-to-end, from defining health signals and debugging failures to building automated remediation systems that operate across millions of GPUs globally. In this role, you will: Define and maintain health signals across GPUs, CPUs, networking, and platform infrastructure. Build and evolve health checks that detect, remediate, and verify failures at scale. Ensure critical health checks execute with minimal latency to maximize workload uptime. Investigate hardware failures and system-level issues across large-scale compute environments. Own node lifecycle workflows including drain, quarantine, repair, RMA, and return-to-service processes. Build automation and tooling that enables global cluster management with minimal manual intervention. Partner with workload, reliability, and provider teams to integrate health signals into training and inference system