Storage And Backup Engineer in San Francisco

About the Team OpenAI's data and storage infrastructure spans data platforms, online databases, and file/object storage. These systems underpin data ingestion and processing, durable persistence, indexing and retrieval, and product file experiences. As frontier models and agents evolve how they use memory, history and snapshots, the underlying architecture increasingly shapes the capabilities products can deliver—and their latency, reliability, cost and efficiency. About the Role We are looking for a technically deep TPM to independently define and lead multiple programs across data platforms, online databases and storage infrastructure. You will connect model, product and data-consumer requirements to architecture, and work with the relevant engineering teams to take new capabilities through production adoption and repeatable expansion. The design scope is exabyte-scale storage and infrastructure spanning multiple millions of CPU cores. The challenge is not simply forecasting more resources: it is making complete, workload-ready capacity repeatable, with a clear path from product requirements through architecture, deployment and validation. A data pipeline, database query, file operation or execution snapshot can affect whether a product or agent succeeds; you will connect those outcomes to the systems underneath. 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: Translate model, product and data-platform needs into precise access patterns, consistency, durability, freshness, availability and scalability requirements. Connect memory, history, retrieval and resumable work to capability and end-to-end latency. Partner with engineering to transform data and storage architecture into repeatable scale units: standardized provisioning, placement, routing, data movement and readiness checks that bring storage, compute and networking online together.

About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About the role The Events Analytics Platform (EAP) team is responsible for the infrastructure that powers all of Sentry's time-series data and searching capabilities across billions of events with sub-second latency. We started this initiative by building Snuba, the primary storage and query service for Sentry's event data powered by ClickHouse, and we are now focused on unlocking deeper visibility and reporting across the terabytes of event data our users generate. As a Senior Software Engineer, you will lead efforts to push the boundaries of data visibility at Sentry. You will do this by expanding the capabilities of our search infrastructure, building new capabilities on top of our state-of-the-art storage layer and increasing the performance and integrity of Sentry’s core data services. You will also help shape Infrastructure's technical direction at Sentry and collaborate with Product and other Engineering teams to turn that vision into a reality. If you want to solve the hard problems that come with scaling event data into the petabyte range, this could be the job for you. In this role you will: Expand EAP's ability to deliver data at world-class speed and reliability. Architect and automate services and systems to scale reliably under growing demand. Make architectural trade-offs that balance product requirements with engineering constraints. Maintain and grow the team's code quality initiatives by regularly reviewing code and contributing to design decisions. Lead design and discussions around deliverables the team is working towards. Improve the maintainability and developer experience of the codebases EAP owns. Exa

About the Team The Online Data team builds and operates the core online database and indexing services for OpenAI’s production AI applications, including supporting the explosive growth of ChatGPT, the #1 AI app in the world, and Codex, the fastest growing agentic development toolset in the world. Our mission is to ensure the reliability, correctness, and scalability of our online data stack and to curate a comprehensive portfolio of services that matches the relentless ambition of OpenAI, enabling our product and research teams to build 0-100 without getting bogged down in the minutiae of multi-region, multi-cloud, exabyte-scale data infrastructure. About the Role We are seeking an Engineering Manager to lead our Online Data Systems team, responsible for our in-house database and indexing technology. This role is about shepherding a team of world-class engineers tasked with building and operating hyperscale data storage and retrieval technology. You’ll be overseeing the delivery of extremely challenging engineering work in areas like distributed query execution, multi-region federation, self-orchestrating and self-healing services, low-level performance optimization, and more. There are few companies in the world building this kind of technology in-house at this scale where you’ll still be getting in on the ground floor. Instead of being a cog in the machine spending months chasing small optimizations, you’ll play a major part of shaping our future. In this role, you will: Build, lead, and grow high-performing infrastructure engineering teams. Drive the evolution of OpenAI’s in-house online data technologies, our core, hyper-scale database systems, indexing technologies, and vector search. Anchor delivery around measurable reliability goals (SLOs, etc) to ensure system performance and resiliency is above reproach. Champion pragmatic use of agent technology to amplify execution velocity. Reduce operational toil and incident frequency through better abstractions, gua

About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About the role Sentry provides developer-first observability to over 4 million developers worldwide. The Events Analytics Platform (EAP) team is at the heart of that mission: it powers how all of Sentry's event data, such as errors, transactions, spans, profiles, replays, and metrics, is stored, queried, and analyzed. It also powers Sentry's latest AI push, Seer. The EAP team makes it possible for developers to efficiently search and debug across massive volumes of data, providing the context needed to understand and fix issues quickly. This team is also a cornerstone of Sentry's long-term strategy to become a context assembly and telemetry platform that unifies different signals so developers can see the complete picture. As an engineering manager on the EAP team, you will lead a group of engineers building and scaling one of Sentry's most critical data platforms. You will be responsible for driving architectural evolution, ensuring system stability, and mentoring a talented team. This is a highly visible leadership role with direct ties to Sentry's long-term product and platform strategy. What you'll do Grow and develop a team of engineers with high expectations for ownership and impact Set the technical and strategic direction for the team, balancing short-term stability with long-term architectural evolution Drive development of core EAP features, including support for complex analytical queries, dynamic routing logic across fidelity levels, storage and compute separation, and modern patterns for analytical storage Ensure EAP can support the workload demands of AI agents and MCP servers that unlock new AI capabilities fo

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role As an AI Accelerator Systems Software Technical Program manager at OpenAI, you will help bring our chips/system hardware roadmap to life, navigating an array of technical and partnership challenges. We’re looking for people excited to push the frontiers of computing by navigating technical explorations and are passionate about building. 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: Manage the end-to-end software development from design to implementation for our AI acceleration systems, working across technical, cross-functional and external stakeholders Lead planning and scheduling of AI system software designs with our strategic partners and vendors Coordinate and lead internal resources and communication for efficient interaction with partners and vendors. You might thrive in this role if you: Have experience as a software technical program manager for data center system products (server, GPU, TPU, networking, storage and so on) taking products from concept to volume in a data center environment ensuring the systems scale with high quality Know end-to-end software development program management techniques from concept, design, production, deployment into the data center Want to help design some of the world’s largest supercomputing systems, working at the edge of complex hardware challenges Enjoy working with and enabling world-clas

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role As a Hardware Chips Programs Manager at OpenAI, you will help bring our chips hardware roadmap to life, navigating an array of technical and partnership challenges. We’re looking for people excited to push the frontiers of computing by navigating technical explorations and are passionate about building. 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: Manage the design and implementation planning of our ML acceleration hardware, working across technical, cross-functional and external stakeholders Lead planning and scheduling of chip hardware designs with our strategic partners and vendors Coordinate and marshal internal resources and communication for efficient interaction with partners and vendors. You might thrive in this role if you: Have experience as a technical program manager for data center hardware products (server, GPU, TPU, networking, storage and so on) Know the whole end-to-end system program management from concept, design, production, deployment into the data center Have some experience with System SW programs through NPI Want to help design some of the world’s largest supercomputing systems, working at the edge of complex hardware challenges Enjoy working with and enabling world-class AI Researchers and Engineers Are passionate about the technical program function, and enjoy independently owning and delivering on your tea

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? The Data Infrastructure team at Cohere is responsible for the storage and data movement layer underlying every model training run. We're building the unified storage layer that feeds our training workloads. It needs to serve petabytes of training data and model checkpoints fast enough to keep thousands of GPUs busy across several training clusters. In this role, you’d have an opportunity to build this system from the ground up. You’d be a key contributor, working on a problem few teams have had to solve at this scale. In this role, you will: Design, build, and operate the distributed storage system that feeds model training and evaluation. Run this system multiple on Kubernetes clusters at petabyte scale. Work with researchers and training-infra teams on how jobs actually read and write data, and turn that into throughput, latency, and durability requirements Work through the networking, I/O, and consistency problems of moving large datasets and checkpoints across regions and backends, with GPU idle time and time-to-insight as the measures of success You may be a good fit if you have: Strong storage fundamentals,

About the Team The Stargate team is responsible for building the physical infrastructure that powers large-scale AI systems. We design and deliver next-generation data centers optimized for dense compute clusters, advanced networking, and rapidly evolving hardware platforms. This work sits at the intersection of hardware engineering, systems architecture, and infrastructure execution—translating cutting-edge compute roadmaps into scalable, production-ready environments. Our teams partner across silicon vendors, server and storage OEMs, networking teams, and data center engineering organizations to bring new capacity online quickly, reliably, and at global scale. About the Role We are seeking a CPU & Storage Technical Lead to define and drive the server compute and storage architecture strategy for Stargate infrastructure. In this role, you will own technical direction across CPU platforms, memory configurations, local and disaggregated storage systems, and their integration into large-scale AI clusters. You will evaluate vendor roadmaps, lead platform tradeoff decisions, and ensure compute and storage systems are optimized for training, inference, and supporting services. You will work cross-functionally with hardware engineering, performance modeling, networking, supply chain, and deployment teams, as well as external partners such as AMD, Intel, OEMs, ODMs, and storage vendors. This is a highly strategic role for someone who can operate deeply at the component level while also driving long-range infrastructure decisions. Key Responsibilities Own CPU and storage technical strategy for Stargate compute infrastructure across current and future generations. Evaluate CPU platforms across performance, efficiency, memory bandwidth, PCIe topology, cost, and roadmap alignment. Define storage architectures for AI environments, including boot media, local NVMe, shared storage, caching tiers, metadata services, and high-performance data pipelines. Drive server platform de

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 Container runtimes were designed for general-purpose software workloads. AI inference is not a general-purpose workload. Running large models at production scale exposes cracks in every layer of the container stack: runtimes unaware of GPU memory constraints, images that take minutes to pull when a model needs to scale to thousands of replicas, and isolation mechanisms that weren't designed for the multi-tenant serving environments that production AI requires. The tools the industry has relied on for a decade weren't built for this, and patching around those limitations at higher layers only goes so far. Baseten owns the entire pipeline, from the moment a developer pushes a model to the moment a request gets a response. That vertical ownership means we can fix these problems at the root. The Runtime Fabrics team is doing exactly that: purpose-building the container runtime and storage layers for AI inference workloads, led by some of the world's top containerd maintainers. As Engineering Manager of the Runtime Fabrics team, you will lead this work, setting technical direction, growing a world-class team of systems engineers, and ensuring the team's output shapes not just Baseten's infrastructure but the open-source container ecosystem at large. If you've contributed to containerd, runc, or related OCI projects and are ready to lead a team solving some of the hardest problems in infrastructure today, we'd love

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 Baseten is seeking talented and experienced Software Engineers to join our Observability team within the Infrastructure organization. As an early member of the Observability Team, you will be pivotal in building and shaping the observability experience for our internal and external customers. By joining this team, you’ll have a direct impact on the reliability and operational excellence of Basetens product systems. As Baseten scales its infrastructure across different cloud providers and diverse hardware, the volume and complexity of operational data is growing by orders of magnitude. This team is responsible for building high-throughput ingest pipelines, cost-efficient storage, and agentic diagnostic tools to ensure that we can detect, diagnose, and resolve issues in minutes rather than hours, even as the systems they operate become more complex. RESPONSIBILITIES Design and build scalable telemetry ingest and storage pipelines for metrics, logs, and traces across Baseten’s multi-cloud infrastructure Own and evolve core observability platforms, driving migrations and architectural improvements that improve reliability, reduce cost, and scale with organizational growth Build instrumentation libraries, SDKs, and integrations that make it easy for engineering teams to emit high-quality telemetry from their services Drive alerting and SLO infrastructure that enables teams to define, monitor, and respond to reliabi

About the Team OpenAI’s Hardware organization develops system and infrastructure solutions designed for the unique demands of advanced AI workloads. We work closely with architecture, infrastructure, and vendor teams to evaluate system performance and guide critical design decisions. Our team focuses on building and applying performance modeling frameworks to understand system behavior, quantify tradeoffs, and support next-generation infrastructure design. About the Role We are seeking an Performance Modeling Engineer to support the development and application of modeling tools used to evaluate AI system performance and inform architectural decisions. In this role, you will partner closely with Senior Performance Modeling Engineers and the Performance Modeling Lead to analyze system behavior, run simulations and analytical models, and help evaluate tradeoffs across compute, memory, networking, and storage. You will contribute to building modeling frameworks while developing a strong foundation in system architecture and AI infrastructure. This role is ideal for early-career engineers with 1–2 years of experience in software engineering, systems analysis, or performance modeling who are excited to grow in large-scale infrastructure and hardware/software 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. Key Responsibilities Support the development and maintenance of performance modeling tools and frameworks Assist in building models to evaluate system behavior across compute, memory, networking, and interconnect subsystems Help analyze distributed system scaling behavior and identify performance bottlenecks Run simulations and analytical models to support architecture and infrastructure decisions Partner with senior engineers to evaluate design tradeoffs across hardware and system components Interpret modeling outputs and help translate findings into clear recommendations Vali

About the Team OpenAI’s Hardware organization develops system and infrastructure solutions designed for the unique demands of advanced AI workloads. We work closely with architecture, infrastructure, and vendor teams to evaluate system performance and guide critical design decisions. Our team focuses on building and applying performance modeling frameworks to understand system behavior, quantify tradeoffs, and inform next-generation infrastructure design. About the Role We are seeking Performance Modeling Engineers to develop and apply modeling tools that evaluate AI system performance and inform architectural decisions. In this role, you will work closely with the Performance Modeling Lead and partner teams to analyze system behavior, run simulations or analytical models, and help quantify tradeoffs across compute, memory, networking, and storage. You will contribute to building modeling frameworks and applying them to real-world questions that impact system design and vendor decisions. This role is well-suited for engineers with strong software or modeling backgrounds who are interested in developing deeper expertise in system architecture and AI infrastructure. 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. Key Responsibilities Develop and maintain performance modeling tools and frameworks. Build models to evaluate system behavior across: compute, memory, and interconnect subsystems distributed system scaling and bottlenecks. Run simulations and analytical models to support architectural tradeoff analysis. Collaborate with performance modeling lead and system architects to answer forward-looking design questions. Analyze and interpret modeling outputs, translating results into actionable insights. Validate models against real system measurements and workload behavior. Contribute to improving modeling fidelity, usability, and scalability. Qualifications Strong software engineeri

About the Team The Private Computing team works across product, engineering, security, and safety to build advanced privacy products and infrastructure at OpenAI. Our mission is to provide world-class security features to users so their private data remains private, even from OpenAI. We use technologies like confidential computing, trusted execution environments, and end-to-end encryption to ship product features across ChatGPT, the API, and our future consumer devices. About the Role We’re looking for software engineers to design, build, and scale novel privacy features and infrastructure across ChatGPT, API, and future consumer devices. In this role, you will: Ship fast while balancing difficult trade-offs in complex domains Build core abstractions for trusted execution environments and end-to-end-encryption Build product features for private inference and storage across ChatGPT, API, and future consumer devices Update build systems to increase trust and verifiability Integrate with safety and integrity infrastructure Operate systems at scale with high reliability, including an on-call rotation Collaborate with a diverse set of cross-functional teams across product, engineering, security, safety, policy, and legal You might thrive in this role if you: Care deeply about user privacy and security Have 5+ years of experience in professional software engineering Have experience building and scaling confidential computing or encryption technologies in production environments Have experience with Kubernetes and cloud orchestration systems Take pride in building and operating scalable, reliable, secure systems Can collaborate well and drive alignment in the face of difficult trade-offs Are comfortable with ambiguity and rapid change Workplace & Location This role is based in San Francisco, CA. We follow a hybrid model with 4 days a week in the office and offer relocation assistance to new employees. About OpenAI OpenAI is an AI research and deployment company dedicat

About the Team: The Database Systems team specializes in high-performance distributed databases. Our team built Rockset, the real-time search, analytics, and vector database that powers all vector search and retrieval augmented generation (RAG) at OpenAI. In addition to retrieval, as an online database, Rockset powers core functionality across all of OpenAI's product lines and many critical internal use cases. About the Role : We are looking for engineers passionate about distributed systems, close-to-the-metal performance optimization (our core engine is written in C++), and building scalable database infrastructure from the ground up. As an engineer on the Database Systems team, you'll contribute to the core database engine, driving improvements across ingestion, query execution, indexing, and storage. You'll partner with teams across OpenAI to unlock new product capabilities and help scale online database reliability and throughput as usage grows by orders of magnitude. In this role you will: Design, build, and operate high-performance distributed systems Identify and resolve performance bottlenecks to scale infrastructure to the next order of magnitude Define long-term technical direction and guide system evolution Collaborate with product, engineering, and research teams to deliver scalable and reliable infrastructure Dig deep into complex production issues across the stack Contribute to incident response, postmortems, and best practices for system reliability You might thrive in this role if you: Have significant experience building, scaling, and optimizing distributed systems at scale Are curious about database internals, storage engines, or low-latency query systems Enjoy debugging challenging performance issues in complex, high-throughput systems Have experience operating production clusters at scale (e.g., Kubernetes or other orchestration systems) Think rigorously about scalability, correctness, and reliability Thrive in fast-paced environments with high

About the Team The Platform Analytics team builds the systems OpenAI researchers use to understand the quality and behavior of the models we train including what models are doing, why they behave in a particular way, and how that behavior changes across experiments. Neptune is a core part of this work. It ingests, stores, queries, and visualizes large volumes of metrics from pretraining, post-training, and reinforcement learning. Hundreds of researchers depend on these systems in their daily work to compare experiments, debug unexpected behavior, and decide what to try next. Our scope is broader than metrics. We also build platforms that help researchers analyze samples, traces, evaluation results, and other structured or unstructured data through dashboards, APIs, and increasingly agent-driven workflows. These systems need to remain fast, reliable, and understandable as the scale and complexity of research change quickly. We are not trying to become a consulting team that builds a separate solution for every research project. We work directly with researchers to understand recurring problems, then turn them into reusable infrastructure and platform capabilities that many teams can build on. About the Role We’re looking for a hands-on experienced software engineer who can take ownership of a critical system and drive it from problem definition through production adoption. This person should be able to own a platform such as CacheHouse end to end: define its technical direction, design its data model and storage architecture, integrate it with several research dashboards and workflows, guide one or two engineers, and ensure the system works reliably for its users. The right candidate should already bring the technical judgment, ownership, and execution expected at this level. The primary learning curve should be OpenAI’s stack and research problem space, not learning how to lead a complex engineering effort or deliver a production system. You will work directly with