Research Engineer Intern in United States

About the Team The Privacy Engineering team builds the systems and technical foundations that govern how user data is understood, retained, accessed, and used across OpenAI. We partner with Product, Data, Infrastructure, Security, and Legal to translate policy and trust commitments into durable architecture and enforceable controls. Our work spans data inventory and mapping, classification and lineage, retention and deletion, access governance, purpose and usage controls, auditability, and lifecycle automation. We aim to make policy-aligned data handling the default while giving teams clear, reliable primitives for building and operating products at scale. About the Role We are looking for an experienced Software Engineer to drive the architecture and execution of user data governance across OpenAI. You will define technical direction, build shared platforms and controls, and lead cross-functional programs that make data flows discoverable, policies enforceable, and ownership explicit. This role is well suited to a senior engineer who can move between deep systems design and organization-wide influence, turn ambiguous requirements into pragmatic roadmaps, and operate high-trust systems end to end. This position is based in San Francisco. Relocation assistance is available. In this role, you will: Set the technical strategy and architecture for user data governance across data mapping, classification, lineage, retention, deletion, access, and permitted usage. Design and build shared services, APIs, metadata systems, and policy-enforcement mechanisms that make governance controls consistent, scalable, and auditable. Establish reliable inventories of user data, system ownership, data flows, and policy applicability across products, infrastructure, analytics, and research systems. Partner with Product, Data, Infrastructure, Security, and Legal leaders to define decision rights, translate requirements into controls, and drive adoption across teams. Own governance systems

About the Team At OpenAI, we’re building safe and beneficial artificial general intelligence. We deploy our models through ChatGPT, our APIs, and other cutting-edge products. Behind the scenes, making these systems fast, reliable, and cost-efficient requires world-class infrastructure. The Caching Infrastructure team is responsible for building a caching layer that powers many critical use cases at OpenAI. We aim to provide a high-availability, multi-tenant cache platform that scales automatically with workload, minimizes tail latency, and supports a diverse range of use cases. We’re looking for an experienced engineer to help design and scale this critical infrastructure. The ideal candidate has deep experience in distributed caching systems (e.g., Redis, Memcached), networking fundamentals, and Kubernetes-based service orchestration. In This Role, You Will: Design, build, and operate OpenAI’s multi-tenant caching platform used across inference, identity, quota, and product experiences. Define the long-term vision and roadmap for caching as a core infra capability, balancing performance, durability, and cost. Collaborate with other infra teams (e.g., networking, observability, databases) and product teams to ensure our caching platform meets their needs. You Might Thrive In This Role If You: Have 5+ years of experience building and scaling distributed systems, with a strong focus on caching, load balancing, or storage systems. Have deep expertise with Redis, Memcached, or similar solutions, including clustering, durability configurations, client-side connection patterns, and performance tuning. Have production experience with Kubernetes, service meshes (e.g., Envoy), and autoscaling systems. Think rigorously about latency, reliability, throughput, and cost in designing platform capabilities. Thrive in a fast-paced environment and enjoy balancing pragmatic engineering with long-term technical excellence. About OpenAI OpenAI is an AI research and deployment company d

About the Team The Developer Experience team at OpenAI has a singular focus: empowering developers globally. Our mission is to provide every developer and startup on the planet with the most delightful and seamless experience to integrate AI into their applications and products. We ensure developers have the tools, resources, and support they need to unlock AI’s full potential. We create inspiring demos, developer tools, sample applications, and technical content that show developers how to build with Codex and frontier models like GPT-5.6, GPT-Live, and GPT-Image-2 to create powerful agents and AI-native applications. We collaborate closely with product, engineering, research, and GTM teams to ensure the developer journey, from onboarding with Codex to first API call to production deployment, is seamless, effective, and delightful. About the Role As a Developer Experience Engineer, you will create compelling technical content, developer tools, and sample applications designed to inspire developers and enable them to succeed with Codex and OpenAI’s APIs and products for developers. You will engage with developers and technical founders, demonstrating best practices and building innovative applications powered by frontier models, multimodal capabilities, and tools like Codex. We’re looking for people who combine strong technical skills, creativity, and a passion for engaging with and empowering developers. In this role, you will: Develop demos and sample applications that showcase best practices for building with Codex, frontier models, multimodal capabilities, and agents. Create high-quality technical content—including tutorials, blog posts, videos, and code samples—to educate and inspire the developer community about our models, APIs, and Codex. Actively engage with and foster a vibrant local and global developer ecosystem around OpenAI’s platform and products. Represent OpenAI at developer events and online, serving as a knowledgeable and approachable advocate for

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 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

About the Team The GPT Infrastructure team builds systems that turn advances in model inference and optimization into reliable production capabilities. We enable OpenAI workloads to be qualified and optimized across new accelerator platforms without requiring a one-off port and tuning effort for every hardware target. Our work spans distributed systems, model execution, compilers and runtimes, performance engineering, secure partner integrations, evaluation systems, and developer tooling. We build the infrastructure that makes optimization workflows automated, reproducible, and trustworthy. About the Role We are seeking a software engineer to help build the platform that qualifies and optimizes inference workloads across heterogeneous compute environments. You will develop both OpenAI-hosted services and secure partner-side software for running long-lived optimization workflows. These workflows generate candidate kernels, runtime configurations, and serving-stack changes; compile and execute them on target hardware; verify their correctness; measure their performance; and use the results to guide further optimization. You will work across model architecture, distributed execution, compilers, runtimes, networking, and accelerator systems. A central part of the role is turning research prototypes and one-off hardware bring-up efforts into reliable, reusable infrastructure with clear contracts, reproducible results, strong observability, and well-defined security boundaries. Key Responsibilities Design, build, and operate APIs and control-plane services for long-running workload qualification and optimization campaigns, including scheduling, retries, checkpointing, resource budgets, and observability. Build secure partner-side execution and evaluation software that can compile, run, verify, profile, and benchmark candidate artifacts on accelerator hardware. Integrate model workloads, hardware profiles, compiler toolchains, runtimes, serving engines, and distributed-exe

About the Team Our team analyzes inference stack performance across the application, model, and fleet layers to identify bottlenecks and drive faster, cheaper inference. We combine systems profiling, benchmarking, and analysis to understand where time and cost are spent, then turn that understanding into performance optimizations and models that project performance and capacity needs for future launches. About the Role In this role, you will model inference performance across application, model, and fleet layers with higher fidelity. You will build cost-to-serve estimates from microbenchmarks and create tools that help cross-functional teams reason about latency, capacity, utilization, and cost tradeoffs. In this role, you will Build and refine performance models that translate microbenchmark results into cost-to-serve estimates. Analyze inference workloads end to end across applications, models, and fleet infrastructure. Enhance tooling to identify bottlenecks across layers for latency and throughput. Partner with other teams to turn performance insights into concrete improvements and project how future changes affect inference. You might thrive in this role if you: Enjoy reasoning from first principles about distributed systems, model inference, and hardware efficiency. Are comfortable working across abstraction layers, from application behavior to kernels, accelerators, networking, and fleet scheduling. Have deep expertise with performance profiling, benchmarking, analysis, and optimization. Enjoy collaborating with engineering and research teams to improve real production systems. 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 our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve o

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 OpenAI is seeking a Principal Security Engineer to join our Infrastructure Security (InfraSec) team. InfraSec protects the foundations of OpenAI’s research and production environments, spanning GPU supercomputing clusters, multi-cloud infrastructure, datacenters, networking, storage, and the critical services that power our frontier AI models. Our charter includes securing everything from bare-metal hardware and firmware, to Kubernetes clusters and service meshes, to data storage and access pathways for highly sensitive model weights and user data. As a principal engineer, you will set technical direction and drive execution on high-impact infrastructure security programs, partnering across various orgs at OpenAI to deliver durable controls that raise the security bar at OpenAI scale. In this role, you will: Own end-to-end security outcomes for one or more critical infrastructure areas, including multi-quarter strategy, roadmap, and delivery. Design and build security controls across diverse layers (e.g., physical hardware, firmware/BMC, OS, Kubernetes, networks, and CI/CD) to defend against sophisticated adversaries and insider threats. Lead cross-functional programs to deploy security enhancements and control changes across broad-scale infrastructure, balancing security guarantees with reliability and velocity. Take a generalist approach to building security controls, balancing a mix of security expertise and broad technical skillsets

Job Details: Job Description: Intel Corporation is the world's largest semiconductor company and a global leader in computing innovation. Our Quantum Computing Group represents Intel's bold venture into the next frontier of computing technology, leveraging our decades of semiconductor manufacturing expertise to develop silicon-based quantum processors. We're pioneering spin qubit technology in silicon quantum dots, combining cutting-edge quantum physics with Intel's unmatched manufacturing capabilities to create the world's first commercially viable quantum computers. Our collaborative approach spans Intel's research divisions and partnerships with leading academic institutions worldwide. We are seeking an exceptional Quantum IC Substrate Package Layout Design Engineer to join our pioneering quantum computing team in Oregon. In this role you will be responsible for design of large form factor IC substrates within a multidisciplinary team. You'll play a crucial role in advancing Intel's silicon quantum dot technology while contributing to the development of scalable quantum systems that will revolutionize computing. Key Responsibilities - Design of IC substrates using Siemens Xpedition IC package design software - Work closely with stakeholders to meet design requirements - e.g., receive feedback from silicon testchip, signal integrity (SI), power integrity (PI), thermal simulation, and system integration engineers - Deliver and disseminate design milestones to stakeholders - Design validation and verification; including DRC and LVS - Advanced packaging design integration (2.5D and 3D) with IC substrate - Tapeout of designs with substrate suppliers in collaboration with Intel substrate teams - Communicate designs in presentations and relevant documentation - Stay curre

Job Details: Job Description: Note: This role requires regular onsite presence to perform essential job responsibilities. This position drives technology development and enablement for both high-volume manufacturing and future process technologies. Responsibilities include providing process integration and equipment solutions, conducting feasibility studies to meet device performance requirements, and leading the design and development of advanced manufacturing processes. The role involves material selection, parameter optimization, metrology, equipment engineering, and system design to enable new product architectures and functional requirements. The successful candidate will lead pathfinding activities that support process and hardware development for next-generation manufacturing technologies and will contribute to technology roadmaps that enable future product innovation. The role also includes identifying and implementing equipment and process improvements to enhance manufacturing efficiency, optimize production output, and improve operational performance. Qualifications: Minimum Qualifications Master's degree in Electrical Engineering, Mechanical Engineering, Chemical Engineering, Materials Science, or a related STEM field, with 6+ years of semiconductor industry experience , primarily in dry etch process development. Bachelor's degree in Electrical Engineering, Mechanical Engineering, Chemical Engineering, Materials Science, or a related STEM field, with 9+ years of semiconductor industry experience , primarily in dry etch process development. 3+ years of experience collaborating with equipment suppliers (e.g., Lam Research, TEL, Applied Materials, Hit

We anticipate the application window for this opening will close on - 29 Sep 2026 Careers that change lives start here. Medtronic is a global leader in healthcare technology with a Mission to alleviate pain, restore health, and extend life. Our 95,000 employees work across more than 150 countries to put patients first — developing innovative medical technologies that improve the lives of 72+ million patients each year. Your unique talents will help shape the future of healthcare while building a career grounded in purpose, growth, and impact. A Day in the Life The Supplier Quality function partners with Contract Manufacturers (CMs), Original Equipment Manufacturers (OEMs), and critical suppliers to ensure the quality, compliance, and performance of externally manufactured products and components. This team works cross-functionally with Research & Development (R&D), Quality, Regulatory Affairs, and Operations to support product lifecycle activities, drive supplier performance improvements, and maintain compliance with applicable quality system and regulatory requirements. This position may be performed remotely anywhere within the United States. Travel up to 15% is required to support supplier audits, supplier performance reviews, business meetings, and cross-functional initiatives. The Senior Supplier Quality Engineer serves as the primary quality partner for strategic Contract Manufacturers and Original Equipment Manufacturers, ensuring supplier processes, products, and quality systems consistently meet Medtronic requirements. This role drives supplier quality performance, supports product and process changes, leads quality improvement initiatives, and provides technical quality guidance across supplier-related activities throughout the product lifecycle. Primary Responsi

Work Flexibility: Onsite It's Time to Join Stryker! Stryker is seeking a Staff Electrical Engineer to support the design and development of electrical systems and subsystems for complex medical devices. In this role, you will apply your electrical engineering expertise across design, prototyping, testing, troubleshooting, and documentation. You will translate product and customer needs into engineering requirements and robust electrical designs while collaborating closely with cross-functional teams throughout the product development lifecycle. What You Will Do Electrical Design and Development Design and develop electrical components, circuits, and subsystems for medical devices. Translate design inputs and system requirements into electrical engineering specifications and detailed designs. Develop, evaluate, and refine design concepts through analysis, prototyping, simulation, and bench testing. Apply advanced electrical test methods to evaluate component and subsystem performance. Investigate complex technical problems, identify potential solutions, and evaluate design alternatives against product and subsystem requirements. Conduct technical research, analysis, and engineering studies to support design decisions and product development. Assess system-level impacts and dependencies associated with electrical design choices. Verification and Technical Execution Develop and execute test methods to evaluate electrical designs, components, and subsystems. Analyze test results, troubleshoot design issues, and drive problems to root cause and resolution. Apply engineering analysis, modeling, simulation, and statistical methods as appropriate to support design decisions. Partner with other engineering dis

About the team OpenAI’s Forward Deployed Engineering team partners with banks, asset managers, insurers, and private capital firms to deploy production-grade AI systems in high-stakes financial environments. We operate at the intersection of customer delivery and core platform development, embedding deeply with customers to translate frontier model capabilities into reliable, auditable systems that create measurable business impact. Our work turns early deployments into repeatable solution patterns, operating standards, and evaluation practices that scale across regulated financial institutions. About the role We are hiring a Forward Deployed Engineer (FDE) to lead end-to-end deployments of OpenAI’s models inside financial services organizations where correctness, latency, explainability, and control matter. You will work with customers who are experts in investment banking, trading, risk, compliance, underwriting, research, operations, or investment decision-making, translating complex workflows, data constraints, and regulatory requirements into production systems. You will measure success through production adoption, workflow efficiency, risk reduction, revenue impact, and evaluation-driven feedback loops that inform product, model, and GTM strategy. You’ll work closely with Product, Research, GTM, Security, Legal, and GRC to deliver systems that meet enterprise standards for governance, auditability, and operational resilience. You will also play a central role in shaping OpenAI’s Financial Services offering — identifying high-value use cases, defining solution patterns, and building the first repeatable deployments that scale across institutions. Learn more about some of our work with financial institutions . This role is based in New York. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% may be required. In this role, you will Design and ship production AI systems around models, owning integrations,

About us Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation. Job Summary We are seeking an experienced Principal Hardware Diagnostics Engineer to design and develop diagnostics software used to monitor hardware health and diagnose system-level issues across Graphcore’s AI infrastructure platforms. This role focuses on building diagnostics agents, tools, and analytics frameworks that enable engineers and automation systems to identify, isolate, and resolve hardware issues across blade-level servers and rack-scale clusters. The Team Graphcore is a globally recognised leader in Artificial Intelligence computing systems. The company designs advanced semiconductors and data centre hardware that provide the specialised processing power needed to drive AI innovation, while delivering the efficiency required to support its broader adoption. The Systems Engineering and Platform Validation team ensures Graphcore’s AI compute platforms are reliable, diagnosable, and operationally robust at scale. The team co

Power and Performance Validation Engineer About us Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry. As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Super Intelligence and ensure its benefits are accessible to everyone. Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore enjoys a culture of continuous learning and constant innovation. Job Summary Reporting to senior leadership within Architecture and Validation, the Power and Performance Validation Lead will drive validation strategy and execution for advanced AI compute silicon and systems. The role is responsible for leading power, thermal and performance validation activities across pre-silicon and post-silicon environments to ensure products meet efficiency, reliability and scalability expectations. This role requires strong technical expertise and collaboration across multiple engineering disciplines to deliver robust validation methodologies, scalable automation frameworks and actionable performance insights. The Team The Power and Performance Validation team sits within the Architecture and Validation organisation and is responsible for validating the performance, efficiency and thermal behaviour of Graphcore silicon and systems. The team supports the full product lifecycle, from early architectural modelling through to first silicon bring-up, characterization and production readiness. Engineers work closely with cross-functional teams globally to debug compl