Academic Research Partnerships And Programs Lead in United States

About the role We are building a higher education researcher motion that helps funded labs adopt OpenAI across core research workflows. This role will develop relationships with principal investigators, researchers, research software engineers, research computing teams, and university technology leaders, then translate high-value use cases into sustained Pro, Codex, API, and ChatGPT Edu usage. This is a hybrid business development and program management role. You will identify and qualify priority labs, design phased access and fellows programs, run pilots, remove technical and institutional blockers, and create repeatable paths from individual researchers to lab- and institution-level adoption. Additionally, this role will launch and manage a community of researchers with events, communications and support. In this role, you will: Build a pipeline of funded labs, research centers, and technical champions at priority R1 universities; qualify opportunities based on workflow value, funding path, influence, and expansion potential. Run discovery with researchers and university stakeholders to understand workflows, data and security needs, procurement constraints, and success criteria. Design and operate phased access and fellows programs, including eligibility, selection, offer mechanics, onboarding, office hours, community programming, and pilot goals. Translate research workflows into effective use of Pro, Codex, API, and ChatGPT Edu in partnership with Solutions, Product, and Education account teams. Manage pilots end to end, remove trust, funding, and technical blockers, and drive measurable activation, retention, and expansion. Turn early usage into product feedback, workflow documentation, peer proof, case studies, and repeatable enablement. Build clear handoffs and expansion paths from researcher to lab to institution; track fellow selection, activation, retained usage, workflow proof, conversion, and expansion. You might thrive in this role if you: Relevant exp

About the Team The ChatGPT Consumer Marketing team helps people understand, adopt, and get lasting value from one of the world’s most widely used AI products. We work across product, brand, creative, growth, media, lifecycle, research, communications, social, partnerships, and international markets. We bring the voice of users into product decisions and build marketing that makes powerful technology feel clear, useful, and human. About the Role We are looking for an Integrated Marketing Manager to shape how ChatGPT shows up for students and young adults. This is an opportunity to own the strategy end to end—from how ChatGPT supports student and campus life to how it becomes a creative partner as young people pursue their ambitions. You will turn a deep understanding of this audience into positioning, experiences, and programs that earn genuine relevance and participation. Your work will span campus communities, student ambassadors, creators, partnerships, activations, social, paid and owned channels, and the product itself. The right person is an uncommon combination of strategist, operator, and creative thinker. You can uncover and articulate a sharp audience insight, translate it into a compelling plan, bring original ideas to the table, and stand up the work needed to deliver them. You have strong instincts for youth culture, know what will resonate without chasing trends, and can align teams around a clear vision for the audience. This role is based in San Francisco, CA, or New York, NY. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees. In this role, you will: Own the end-to-end integrated marketing strategy for students and young adults, from audience insight and opportunity identification through execution, optimization, and learning. Define the role ChatGPT can play across academic life, campus life, creative expression, and the transition from education to career. Turn audience and cultural insi

About the Team The Product Policy team is responsible for the development, implementation, enforcement, and communication of the policies that govern use of OpenAI’s services, including ChatGPT, GPTs, the GPT Store, Codex, and the OpenAI API. About the Role We are looking for a Research and Advisory Partnerships Lead to build and manage the relationships, partnerships, and advisory structures that bring independent external expertise into our product and policy decisions. 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 identify and engage researchers, civil society organizations, subject-matter experts, and other external stakeholders whose perspectives can strengthen how we develop and deploy AI. You will establish and manage advisory councils, develop research partnerships with the academic community, and work closely with internal teams to translate external insights into actionable guidance. This role sits at the intersection of product, policy, research, and governance. It is well suited to someone who is comfortable building relationships across disciplines and designing processes that ensure outside expertise meaningfully informs internal decision-making. This role can be based in San Francisco or New York City. 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: Develop, launch, and manage external advisory councils and other governance mechanisms that provide structured input on product and product policy. Build and maintain relationships with external experts, researchers, civil society organizations, and other stakeholders who can inform high-priority product and product policy decisions. Identify when external consultation can improve internal decision-making, and develop engagement strategies tailored to specific products, policy questions, and emer

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 Interposer Design Engineer to join our pioneering quantum computing team in Oregon. In this role you will be responsible for physical design and integration of advanced packaging interposers 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 - Physical design and integration of interposers used within advanced packaging for quantum computing - 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 - Design validation and verification; including DRC and LVS - Deliver and disseminate design milestones to stakeholders - Communicate designs in presentations and relevant documentation - Stay current with the latest developments in packaging technology and design techniques by attending conferences and reading papers As

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 Packaging Modeling Engineer to join our pioneering quantum computing team in Oregon. In this role you will be responsible for thermal modeling and simulation of various packaging solutions from silicon redistribution layers to 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 - Thermal modeling and simulation of packaging hardware to understand how heat flows from silicon test chips through various heat spreader hardware - Manage the overall integration of the packaging hardware and enable co-design between relevant stakeholders - Work closely with mechanical designers to define heat spreaders for optimal performance - Deliver and disseminate design milestones to stakeholders - Communicate designs in presentations and relevant documentation - Stay current with the latest developments in packaging technology and design techniques by attending conferences and rea

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: 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 Packaging Signal/Power Integrity (SI/PI) Engineer to join our pioneering quantum computing team in Oregon. In this role you will be responsible for modeling and simulation of various packaging solutions from silicon redistribution layers to 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 - Modeling and simulation of packaging hardware including redistribution layers (e.g., interposers, RDLs), IC substrates, and silicon testchip routing - Work closely with stakeholders to meet design requirements - requirements come from both theory and hardware teams. In some pathfinding projects, you will be asked to participate in creating requirements and novel packaging hardware. - System-level modelling and simulation; end-to-end simulations to ensure designs meet system-level requirements - Deliver and disseminate design milestones to stakeholders - Communicate designs in pr

About the team OpenAI’s mission is to build safe artificial general intelligence (AGI) which benefits all of humanity. This long-term undertaking brings the world’s best scientists, engineers, and business professionals into one lab together to accomplish this. In pursuit of this mission, our Go To Market (GTM) team is responsible for helping customers learn how to leverage and deploy our highly capable AI products across their business. The team is made of Sales, Solutions, Support, Marketing, and Partnership professionals that work together to create valuable solutions that will help bring AI to as many users as possible. About the role As an Account Director focused on Healthcare, you will own executive-level relationships with leading healthcare organizations, including integrated delivery networks (IDNs), health systems, academic medical centers, health insurers, digital health companies, and healthcare technology providers. You’ll help these organizations safely and effectively deploy OpenAI’s technology to improve clinical and administrative workflows, enhance patient and provider experiences, automate operational processes, and accelerate enterprise-wide AI adoption. This role blends enterprise sales expertise, technical depth, business acumen, and relationship-driven selling. You will collaborate closely with researchers, engineers, and healthcare-focused solution strategists to design secure, compliant, and high-impact AI deployments. This role is based in San Francisco. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees. In this role, you’ll: Manage a focused portfolio of large healthcare provider, payer, and healthcare technology accounts, developing long-term strategic account plans. Lead complex, multi-stakeholder sales cycles spanning clinical, operational, IT, digital transformation, and executive leadership teams. Partner with Solutions and Research Engineering to design pilots that demon

NVIDIA is at the forefront of the AI and robotics revolution, and NVIDIA’s robotics teams are on a mission to build the essential technology that can enable any company to become a robotics company. The Seattle Robotics Lab is uniquely positioned at the intersection of open academic research and real-world industry impact, pursuing fundamental and applied robotics research across the full robotics stack, including perception, planning, control, reinforcement learning, imitation learning, simulation, and robotics foundation models. This research aims to transform research paradigms, transfer into NVIDIA’s robotics and simulation products, and create new robotics markets for the world. The Seattle Robotics Lab has published over 500 research papers, including many influential works that have been presented at top robotics, AI, and computer vision conferences. These works include BayesSim , cuRobo , DeXtreme , DiSECT , Factory , GraspNet , IndustReal , ITPS , LAPA , <a href="h

The Applied AI team designs and builds algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as anomaly detection, error outliers and faulty deployment analysis. As an Applied Scientist you will work on building models and algorithms for machine learning powered features within the Datadog platform. You will work closely with our engineering and product partners to explore, build, scale and deliver these features that we incubate within the Applied AI team. At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together. We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them. What You’ll Do: Design solutions for our different use cases. You will research and benchmark relevant algorithms to find the best fit for our use-cases Leverage machine learning algorithms and statistical techniques to build new scalable product features Develop, deploy and monitor new and existing features to production Participate in our journal club by reading and presenting the latest academic research papers to the team Explore, analyze and tell the story behind high volumes of data flowing through Datadog systems Maintain and monitor the models, services and infrastructure owned by your team Participate in your team’s on-call rotation Who You Are: You have a BS/MS/PhD in a Computer Science, Engineering, Machine Learning or related scientific field or equivalent experience You have experience working with high-scale systems and datasets including building models, applying machine learning to real business problems, and writing production data pipelines You can explain complex ideas and algorithms to non-technical audiences You care about code simplicity and performa

Datadog AI Research — Scholars Program with Carnegie Mellon University Datadog AI Research (DAIR) is partnering with Carnegie Mellon University to support a small number of PhD students working on open research problems grounded by ongoing efforts at Datadog/DAIR. You will frame a problem, run your own experiments, and write up what you find, with compute and data at a scale most academic labs cannot provide. You will collaborate with colleagues working on the same questions. The Lab And The Research: DAIR is an industrial research lab motivated by practical challenges in observability and software operation: detecting and diagnosing failures, understanding complex production environments, and helping engineers operate software more effectively. The lab focuses on creating specialized foundation models, post-training and evaluating AI agents, and building frontier-scale machine learning systems. By combining fundamental research with Datadog's large-scale, real-world data and infrastructure, the lab develops new AI capabilities and translates them into practical systems with meaningful impact. Internship projects are shaped with your DAIR mentor and your CMU faculty advisor. You do not need prior experience with observability, monitoring, or infrastructure. What You'll Do: Own a research project end to end: framing the question, running the experiments, writing it up Work directly with a DAIR mentor engaged in the same problem, and stay connected to your advisor and lab Publish, and use the work toward your dissertation See research reach production, when it works Who You Are: Currently enrolled in a PhD program at Carnegie Mellon in machine learning, computer science, statistics, or a related field Depth in at least one area relevant to the research above Comfort running real experiments — training models, working with GPUs, reading and reimplementing recent papers Evidence you can do research: conference or workshop papers, preprin

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 PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This internship is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments. Preferred Qualifications: Currently pursuing a PhD in computer science, machine learning, or a related field. A demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas. Experience developing and evaluating large-scale models or machine learning systems. Familiari

We are seeking a mission-driven Developer Relations Manager focused on Foundational AI Research to engage leading academic labs advancing the next generation of AI models, systems, and methods. In this role, you will work directly with top researchers building frontier AI systems, including large language models, multimodal models, reasoning systems, training methods, inference systems, model serving, and scalable AI infrastructure. You will help researchers adopt NVIDIA’s AI and accelerated computing platforms to push the boundaries of model performance, efficiency, and scale. The ideal candidate brings deep technical credibility in foundational AI, strong research engagement experience, and hands-on expertise in either AI inference research or AI training research. What you'll be doing: Serve as a trusted technical advisor to leading academic AI labs working on foundation models, LLMs, multimodal AI, reasoning, training, inference, and AI systems. Identify high-impact research workloads where NVIDIA software, systems, and accelerated computing platforms can advance model performance, scale, and efficiency. Engage principal investigators, postdocs, graduate researchers, and lab leadership to understand research goals, technical blockers, infrastructure needs, and collaboration opportunities. Track frontier AI research across papers, benchmarks, open-source projects, and academic labs to identify emerging trends and future platform opportunities. Partner with Research Account Managers, Solution Architects, Product, Engineering, and Business Development teams to support researcher adoption and long-term engagement. Represent researcher needs internally by translating academic feedback into actionable insights for product roadmaps, developer programs, education, and platform strategy. Support NVIDIA participation in major AI, ML, and systems research venues through technical content,

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: Most of the value of owning a model shows up at serving time. We're building a platform that covers the whole life of an LLM -- train it, deploy it, observe it -- and inference is where teams feel the difference every day. We already run elastic inference, sandboxes, distributed volumes, and multi-node training, and we control the infrastructure underneath, so the serving stack is ours to shape rather than something we resell. You will do hands-on inference research at Modal, working with the research lead to pick high-impact bets and owning them end to end. The bets that matter most are the ones that move cost per token and tail latency on the workloads our customers actually run. What you'll do: Own end-to-end inference research bets: speculative decoding, disaggregated prefill/decode, quantization (FP8, INT4), KV-cache and memory management, autoscaling for spik

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're building a platform that covers the whole life of an LLM: training it, deploying it, and observing it in production. We already run multi-node training, elastic inference, sandboxes, and distributed volumes, and we control the infrastructure underneath. We’re looking for research depth in post-training to sit alongside our systems and product work. What you'll do: We are looking for research scientists with a strong track record in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This role is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustnes