Compute Strategy And Transactions in United States

About the Team OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI. About the Role As an Engineer on our hardware optimization and co-design team, you will co-design future hardware from different vendors for programmability and performance. You will work with our kernel, compiler and machine learning engineers to understand their unique needs related to ML techniques, algorithms, numerical approximations, programming expressivity, and compiler optimizations. You will evangelize these constraints with various vendors to develop and influence future hardware architectures towards efficient training and inference on our models. If you are excited about efficiently distributing a large language model across devices, dealing with and optimizing system-wide/rack-wide networking bottlenecks and eventually tailoring the compute pipe and memory hierarchy of the hardware platform, simulating workloads at different abstractions and working closely with our partners, this is the perfect opportunity! 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. Key Responsibilities Co-design future hardware for programmability and performance with our hardware vendors Assist hardware vendors in developing optimal kernels and add support for it in our compiler Develop performance estimates for critical kernels for different hardware configurations and drive decisions on compute core and memory h

About the Team The OpenAI Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples’ lives. About the Role As a Research Engineer, Distributed Data Systems, you will design and scale the infrastructure that powers large-scale multimodal training and evaluation at OpenAI. You’ll manage distributed data pipelines, collaborate closely with researchers to translate requirements into robust systems, and harden pipelines that serve as the backbone for OpenAI's rapid iteration cycles. We’re looking for engineers who are detail-oriented, have strong experience with distributed systems, and excel at building reliable infrastructure in high-stakes environments. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: Design, build, and maintain data infrastructure systems such as distributed compute, data orchestration, distributed storage, streaming infrastructure, machine learning infrastructure while ensuring scalability, reliability, and security. Ensure our data platform can scale by orders of magnitude while remaining reliable and efficient. Partner with researchers to deeply understand requirements and translate them into production-ready systems. Harden, optimize, and maintain critical data infrastructure systems that power multimodal training and evaluation. You might thrive in this role if you: Have strong experience with distributed systems and large-scale infrastructure with a strong interest in data. Are detail-oriented and bring rigor to building and maintaining reliable systems. Demonstrate excellent software enginee

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 OpenAI is developing custom silicon to power the next generation of frontier AI models. We’re looking for experienced Design Verification (DV) Engineers to ensure functional correctness and robust design for our cutting-edge ML accelerators. You will play a key role in verifying complex hardware systems—ranging from individual IP blocks to subsystems and full SoC—working closely with architecture, RTL, software, and systems teams to deliver reliable silicon at scale. In this role you will: Own the verification of one or more of: custom IP blocks, subsystems (compute, interconnect, memory, etc.), or full-chip SoC-level functionality. Define verification plans based on architecture and microarchitecture specs. Develop constrained-random, directed, and system-level testbenches using SystemVerilog/UVM or equivalent methodologies. Build and maintain stimulus generators, checkers, monitors, and scoreboards to ensure high coverage and correctness. Drive bug triage, root cause analysis, and work closely with design teams on resolution. Contribute to regression infrastructure, coverage analysis, and closure for both block- and top-level environments. You might thrive in this role if you have: BS/MS in EE/CE/CS or equivalent with 3+ years of experience in hardware verification. Proven success verifying complex IP or SoC designs in industry-standard flows Proficient in SystemVerilog, UVM, and common simulation and debug tools (e.g., VCS, Questa, Verdi). Strong knowledge

About the Team The Statsig team within OpenAI owns the experimentation, rollout, dynamic configuration, and analytics infrastructure that sits on the launch path for OpenAI products. Our systems help teams ship safely, evaluate product and model changes in production, and make high-confidence decisions from real-world usage. Statsig began as an independent company built around experimentation, feature management, and product analytics at scale. After Statsig joined OpenAI, the team began the next chapter: bringing that platform expertise and infrastructure into OpenAI as the experimentation and rollout foundation for every product we ship. This is infrastructure with a very direct product consequence. Teams working on ChatGPT, Codex, model measurement, consumer experiences including ads, business subscriptions, developer products, and shared platform systems depend on Statsig to evaluate configurations, move traffic safely, ingest experiment data, serve analytics, and roll changes forward or back when production reality demands it. We are at a critical point in the platform journey. Adoption is accelerating quickly across OpenAI, and the systems that were already important are becoming load-bearing for how the company launches. The infrastructure needs to stay fast under sharply increasing evaluation volume, reliable when more services depend on it, observable enough to debug quickly, and efficient enough to support OpenAI-wide scale. Recent SDK and server-side infrastructure work has already produced measurable wins in latency, reliability, memory usage, and compute efficiency across important services. The next phase is to make those gains systematic: a platform that can absorb rapidly growing product velocity while preserving low latency, data quality, operational safety, and developer trust. Based out of OpenAI's Bellevue office, we are a close-knit team that values in-person collaboration, technical depth, operational ownership, and building infrastructure that

About the Team Security is foundational to OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity. The Security organization protects OpenAI’s technology, people, and products by building and operating deeply technical systems that must work reliably at massive scale. Our work underpins OpenAI’s commitments around safety, privacy, and security across research, products, and emerging platforms. The Host Assurance team exists to make bare metal a dependable, scalable foundation for OpenAI: secure by default, verifiable in practice, and resilient across providers and operating models. We operate at the trust boundary between physical hardware and cloud-scale orchestration, ensuring that hosts are eligible to safely run workloads with predictable security properties and auditability. About the Role OpenAI is seeking a Security Engineer, Host Assurance to help build the trust foundations for bare-metal platforms across OpenAI’s global infrastructure. This is a deeply hands-on engineering role for a builder who can design, implement, and operate the core security infrastructure that establishes trust in hardware platforms before they are eligible to run workloads. Success in this role requires strong technical judgment, the ability to work comfortably at low levels of the stack, and a practical mindset for building systems that are secure, reliable, and usable in fast-moving production environments. The systems you build will sit on the critical path of OpenAI’s frontier infrastructure investments and will directly shape how large amounts of compute are brought online - securely, responsibly, and at global scale - underpinning long-lived commitments around privacy, security, and reliability. You will partner closely with infrastructure, research, and confidential computing initiatives—including novel hardware platforms and emerging deployment models– to make the secure path the easiest path. This role is well suited for engineers who enjo

About the Team The Future of Computing Research team is an applied research team in the Consumer Devices group focused on developing new methods and models to support our vision as we advance forward in our mission of building AGI that benefits all of humanity. About the Role As a Technical Lead on the Future of Computing Research team, you will work together with both the best ML researchers in the world and the greatest design talent of our generation to push the frontier of model capabilities. This role is based in San Francisco, CA. We follow a hybrid model with 3 days a week in the office and offer relocation assistance to new employees. In this role, you will: Evaluate and select silicon platforms (GPUs, NPUs, and specialized accelerators) for on-device and edge deployment of OpenAI models. Work closely with research teams to co-design model architectures that meet real-world deployment constraints such as latency, memory, power, and bandwidth. Analyze and model system performance, identifying tradeoffs between model design, memory hierarchy, compute throughput, and hardware capabilities. Partner with hardware vendors and internal infrastructure teams to bring up new accelerators and ensure efficient execution of transformer workloads. Build and lead a team of engineers responsible for implementing the low-level inference stack, including kernel development and runtime systems. Run through the necessary walls to take nascent research capabilities and turn them into capabilities we can build on top of. You might thrive in this role if you: Have experience evaluating or deploying workloads on GPUs, NPUs, or other specialized accelerators. Understand the performance characteristics of transformer models, including attention, KV-cache behavior, and memory bandwidth requirements. Have designed or optimized high-performance compute systems, such as inference engines, distributed runtimes, or hardware-aware ML pipelines. Have experience building or leading teams work

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 We are seeking an experienced SoC Architect to lead the definition and development of next-generation custom AI silicon for edge deployments. This role will be responsible for shaping the architecture of highly efficient, high-performance SoCs optimized for machine learning inference and on-device intelligence. You will work cross-functionally with internal engineering teams and external ecosystem partners to translate product requirements into scalable silicon solutions, driving execution from concept through delivery. In this role you will: Define the architecture and technical roadmap for custom SoCs targeted for edge applications. Drive system-level tradeoff analysis across compute, memory, interconnect, power, thermal, and cost constraints. Architect energy-efficient ML compute subsystems optimized for inference workloads and real-world deployment environments. Collaborate with internal hardware, software, systems, and product teams to align architecture with platform needs. Partner with external silicon vendors, IP providers, and manufacturing partners to execute development plans. Lead hardware/software co-design efforts to maximize performance per watt and end-to-end system efficiency. Guide implementation teams through microarchitecture, RTL development, validation, and bring-up phases. Operate effectively in agile development environments and help teams deliver against aggressive schedules and milestones. You might thrive in this role if: Proven exper

About the Team The Statsig team within OpenAI builds the experimentation, feature rollout, dynamic configuration, and analytics systems that help OpenAI ship products with speed, safety, and evidence. Our work sits on the critical path for how product, engineering, research, and go-to-market teams learn from real-world usage and make high-confidence decisions. Statsig began as an independent company focused on helping builders move faster through trustworthy experimentation and feature management. After Statsig joined OpenAI, the team began the next chapter: bringing that deep product expertise, customer intuition, and mature platform infrastructure into OpenAI as the experimentation and rollout platform for every product we ship. Today, we support teams across ChatGPT, Codex, model measurement, consumer experiences including ads, business subscriptions, developer products, and the shared infrastructure that connects them. These teams rely on Statsig to safely introduce new capabilities, compare product and model behavior, measure impact, and roll changes forward or back with confidence. We are at a defining moment in the platform journey. OpenAI has the data, product surface area, and pace of innovation to learn faster than almost any organization in the world, but that potential only becomes real if teams can experiment responsibly, measure clearly, and roll out changes safely. Adoption of the platform is accelerating rapidly across the company, and recent SDK and server-side infrastructure work has already produced measurable wins in latency, reliability, memory usage, and compute efficiency for important services. Based out of OpenAI’s Bellevue office, we are a close-knit team that values in-person collaboration, urgency, craft, and impact. We build for other builders, and the best version of this team is one where every OpenAI product team can move faster because the experimentation and rollout layer is dependable, fast, and easy to use. About the Role We are l

About Ema Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale. The residency You own one hard problem end to end. You write the proposal, build the system, design the evaluation, ship behind a gate, and finish with a write-up of what turned out to be true, including the parts that didn't work. You'll sit in the production codebase with a senior mentor and real production data. Recent residents have shipped self-improving harnesses, inference-cost work, agent memory, and eval infrastructure. Your project gets scoped with you, not handed to you. The problem space The loop we care about: production traces become data, data becomes training and evaluation, and better agents produce better traces. Projects live somewhere on that loop. Harness and inference-time work. Context engineering, tool and skill design, orchestration, and deciding where extra inference compute actually pays. Self-improvement loops run behind hard fences. Post-training for agents. SFT on curated trajectories, preference optimization, RL on real agent tasks. Reward design where outcomes are verifiable, process vs. outcome supervision, distilling frontier behavior into cheaper models. Environments and rewards. Turning enterprise workflows into training and eval environments: fi

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. Senior Software Engineer, Snowflake Natsec Running Snowflake in public sectors in different countries and regions, even in different industry verticals, requires us to build a compliant, secure, and auditable infrastructure. Many key design decisions are deeply rooted in the Snowflake product architecture. As a Senior Software Engineer, you will be responsible for leading several key areas and collaborating with various engineering groups in addition to the Public Sector team. To be successful in the area, you will need to have (and continue to build) a broad and in-depth knowledge base on cloud infrastructure, privacy, and governance, compliance controls, data security and data residency in various aspects of Snowflake. AS A SENIOR SOFTWARE ENGINEER AT SNOWFLAKE YOU WILL: Solve real business needs at large scale by applying your software engineering and analytical problem solving skills. Design, implement and maintain scalable distributed systems for our cloud automation platform that include cloud control plane, Kubernetes container platform and traffic and networking. Work directly with customers to quickly understand their critical problems and design and implement solutions Deploy and maintain availability of cloud compute servers and Kubernetes cluster that power the

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. Our team builds and operates Snowflake's Streaming Platform — the core infrastructure responsible for bringing data into Snowflake continuously and at scale. This includes Snowpipe Streaming and Datastream, services used by large set of enterprise customers to move mission-critical data in real time. Data is the fuel powering the new enterprise AI world, and we are the team responsible for getting it there reliably, continuously, and fast. Responsibilities Design, build, and maintain core components of Snowflake's streaming ingestion platform Improve service reliability, scalability, and latency under high-throughput production workloads Debug and resolve incidents in a distributed, multi-tenant cloud service Contribute to the design and evolution of streaming APIs, SDKs, and server-side protocols Write comprehensive tests including unit, integration, and chaos/fault-injection scenarios Collaborate with partner teams (storage, query, compute) on cross-cutting platform concerns Participate in code reviews, on-call rotations, and architecture discussions Required Qualifications 3–5 years of software engineering experience on large-scale distributed systems or cloud services Strong proficiency in Java or C++ Deep understanding of distributed systems concepts: consistency, faul

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. Senior Software Engineer — Cortex Training The Snowflake ML Platform team's mission is to let customers run their most demanding ML/AI workloads inside Snowflake. Cortex Training is our LLM post-training platform: it turns scarce, expensive GPU capacity into a simple, composable service, so customers can adapt open-weight foundation models to their own business problems while we handle the hard distributed-systems parts, including scheduling, orchestration, multi-node training and inference, fault tolerance, and throughput. The platform already runs post-training at scale. Under the hood, it decouples GPU computation from the training loop and exposes it as primitive APIs that compose into everything from SFT to full RL workflows. You'll work alongside a team that ships fast & sweats reliability and the researchers behind DeepSpeed. We're looking for an engineer who thrives in the ML infrastructure layer and brings a solid understanding of LLMs and post-training to help us scale and grow it. YOU WILL: Design and build across the full stack — from the public training APIs and SDK through the control plane to the GPU data plane. Scale the distributed systems that make GPU compute serverless — multi-tenant scheduling, placement, and capacity-aware routing across regional G

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. We are hiring a Principal Engineer II to architect the core data processing engine of the Snowflake Data & AI Cloud. At Snowflake, we believe that high-performance, unified compute fabrics are the indispensable building blocks for Agentic AI. Autonomous agents require more than just models; they require a high-fidelity, low-latency state layer to reason, act, and persist context. This role is not about building traditional data processing pipelines or legacy ETL/ELT workflows; it is about building the core distributed systems and atomic primitives that make those agentic workflows possible. In this role, you will be a lead architect of the Snowflake Data Transformation Engine. You will design and implement the fundamental transformations infrastructure—Stateful Stream Processing Engines, Incremental View Maintenance Engine, Materialization Internals, and the Distributed Orchestration Fabric. Our solid foundation supporting the seamless transition for enterprises between batch and streaming through Dynamic Tables, Streams & Tasks, and DBT Projects is the starting point. Your architectural work will extend the reach of the core engine to accelerate and support the massive scale of the Snowpark and Spark ecosystems. You are building the systems that allow both data eng

Computer Generated Image Producer Description - HP Creative Services is expanding its CG production team, and we’re looking for a CG Producer who can support the creation of high-quality computer-generated imagery across a diverse range of device types — including laptops, desktops, accessories, and peripherals. This role is ideal for a technical project manager or CG pipeline operator with 4–7 years of experience who is ready to deepen their expertise in structured CG workflows, CAD/GP file handling, and AI-assisted imagery production. You’ll work closely with CG Designers, Worldwide Marketing, and product teams to facilitate the creation of accurate, brand-aligned imagery that supports global launches and campaign needs. Responsibilities Oversee day-to-day CG production workflows across multiple device categories with varying form factors. Gather, review, and validate CAD/GP files and other technical inputs required for CG asset creation. Maintain awareness of configuration changes and product updates, ensuring CG assets reflect accurate specifications. Monitor production progress using Workfront, ensuring teams and projects remain aligned and serve as point of escalation for design, marketing, and CG. Partner with Worldwide Marketing, GBU, and creative partners to align on imagery needs and delivery timelines as well as serve as bridge between marketing and agency partners. Participate in product kickoff meetings to understand configuration requirements, launch milestones, and creative direction. Support creative agency HP DAM asset transfer, resulting in accuracy of CG asset metadata and downstream asset availability. Leads budget and expense management, manages project budgets as assigned and handles assigned agencies or contractors as needed, including Purchase Order creation and quarte

About the team The Computer Use and New Interfaces team is focused on discovering and building the next generation of AI-native interfaces. We believe that the value of AI is increasingly constrained not by model capabilities, but by the ways people interact with those capabilities. Our mission is to create new interaction paradigms that unlock the full potential of AI and integrate it more deeply into people's lives and work. Our team does both near-term product development and longer-term product incubation that can influence experiences across ChatGPT, Codex, future OpenAI products, and emerging device platforms. We work in a highly collaborative, design-driven environment where engineering, product, and design operate as one team. We value rapid experimentation, prototyping, and iteration, creating the shortest possible path between an idea, a working system, and a product decision. About the role We're looking for exceptional engineers who are excited to invent entirely new ways for people to interact with AI. You'll work at the intersection of engineering, product, and design to explore, prototype, and build novel interface concepts that push beyond traditional software paradigms. This role requires comfort with ambiguity, strong product instincts, and a willingness to move fluidly between experimentation and production systems. You'll help shape both the capabilities and the user experiences that define how people engage with AI. In this role you will: Design, prototype, and build novel AI-native interfaces and interaction models. Develop foundational technologies and frameworks that enable generative UI and computer use experiences. Collaborate closely with designers, product thinkers, and engineers to rapidly explore and validate new concepts. Build end-to-end prototypes and production systems that can influence future OpenAI products. Contribute to platform technologies that can be adopted across multiple product surfaces. Help establish technical directio