Research Science Intern in United States

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

Who We Are Notion is the collaborative AI workspace where teams and agents think together . We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. This internship will take place from January 25 - April 16 and you will need to be able to work out of our SF office during this time. What You'll Achieve: Conduct data analyses to gain insights about Notion and use these insights to uncover opportunities for improvements in our product and business. Communicate these insights with actionable recommendations to cross-functional teams (insights are useful, impact is even better!). Work with cross-functional partners across the product and business to learn about their functions and use data to advance their respective areas. Create metrics and build dashboards to monitor the growth and health of Notion. Communicate insights and recommendations effectively to leadership and have an impact on strategic decision-making. Qualifications: Pursuing a bachelor's or master's in a quantitative field such as Economics, Statistics, Applied Math, Engineering, Computer Science, or Natural Sciences. Must graduate before December 2027. This internship will take place from January 25 - April 16 and you will need to be able to work out of our SF office during this time. Previous research or internship

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. About the Team The Fraud Partnerships Pod is responsible for overseeing all traditional business development and partnerships for the Anti-Fraud Product Area (PA) at Plaid, focusing primarily on our product partnerships and data acquisition (supply side) efforts. The team supports all products under the Fraud Product Area's purview: IDV, Monitor, Layer, and Protect. About the Role You will have an opportunity to lead the strategy and execution of our supply side and "data in" efforts for our Fraud product area across four products (Plaid IDV, Monitor, Layer, and Protect). You will own all critical data partner relationship management with key stakeholders and be expected to grow them over time. You will be the internal quarterback driving alignment with key major internal stakeholders at Plaid, including Finance, BizOps, Commercial, Legal, and Risk. What You'll Do Spend the majority of your time working hand in hand with product leadership as well as other cross-functional leaders to operationalize our data partners for all fraud products at Plaid. Help our data science and research teams define the source data ecosystem for IDV and Protect according to our 3-year strategy. Help e

About the Team The Support Automation team at OpenAI scales the organization by applying cutting-edge AI models to real-world challenges, automating and enhancing work across the organization. From customer operations to engineering, we develop an ecosystem of automation products that empower our colleagues and drive impact. We're passionate about crafting products that serve those around us, blending rapid prototyping with a focus on long-term quality and reliability. By creating reusable solutions, we create patterns that can be applied across diverse domains within OpenAI. TLDR: this team leverages OpenAI technology to improve OpenAI, and you’ll have the opportunity to leverage the full extent of our tech (both public and pre-released) to accomplish this mission. About the Role We’re looking for a Backend Software Engineer with experience working in ML/LLM-heavy domains to help to design and build an evals infrastructure that measures the quality of OpenAI’s support automation. This is a deeply technical and highly cross-functional role where you’ll build robust systems and backend services that serve as the foundation for how knowledge is created, accessed, and applied across OpenAI. The role will especially focus on working closely with Data Science and Research partners to design and build evals at scale. In this role, you will: Design eval pipelines that are reliable, reproducible, and extendable Build the infrastructure for continuous eval monitoring frameworks (regression/drift monitoring, building robust golden datasets) along with feedback loops that ultimately strengthen support automation Design, build, and maintain backend services and APIs to support intelligent automation and knowledge systems Integrate and structure data across internal platforms, transforming it into formats optimized for use by downstream systems and AI workflows. Collaborate closely with data, research, and engineering teams to integrate OpenAI models into high-leverage workflows

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

Job Details: Job Description: Contributes to module process development, process integration flows, and equipment configuration for manufacturing modules. Supports yield improvement initiatives by analyzing defect data, conducting experiments, and assisting in risk assessments. Electrcial characterization of transistors and analysis of data to help yield or improved process conditions. Collaborates with engineering teams to optimize metrology strategies and troubleshoot process flow issues. Contributes to continuous improvement efforts in high volume manufacturing environments. As an intern, learns and applies knowledge, builds skills, and explores future career opportunities through hands on experience and projects that support Intel business goals in a collaborative environment Qualifications: You must possess the below minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the minimum qualifications and are considered a plus factor in identifying top candidates. Experience listed below would be obtained through a combination of your schoolwork, classes, research, relevant previous job, and/or internship experiences. Minimum qualifications: Must be pursuing a PhD degree in a hard science discipline such as Electrical Engineering, Physics, Materials Science.

Our work at NVIDIA is dedicated towards a computing model focused on visual and AI computing. For two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics, with our invention of the GPU. The GPU has also shown to be spectacularly effective at solving some of the most complex problems in computer science. Today, NVIDIA’s GPU simulates human intelligence, running deep learning algorithms and acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. We are looking to grow our company and teams with the smartest people in the world and there has never been a more exciting time to join our team! The AI Infrastructure Product Design team creates software used by engineers and researchers to prepare data, run complex workflows, and understand results. This internship offers ownership of a defined product problem from early research through a tested design and implementation handoff. Designers on this team often move between Figma and working HTML prototypes, and may hand off HTML directly to engineering. This work calls for a high standard of visual and interaction design alongside technical fluency. The role is a good fit for someone who enjoys making technically complex systems easier to understand and who uses large language models and software agents thoughtfully as part of their design and prototyping process. What you will be doing: Own a focused design project for an internal AI infrastructure product, from understanding the problem through a validated design and implementation handoff. Interview engineers and researchers, map their workflows, and turn the findings into clear product requirements, user flows, and interaction models. Create precise, implementation-ready interface designs and interactive prototypes in Figma and HTML/CSS, with careful attention to typography, hierarchy, spacing, visual consistency, interactio

Chicago Trading Company (CTC) is a premier proprietary trading firm specializing in options market making. Our collaborative culture fuels innovation in quantitative research, systematic trading strategies, and cutting-edge trading technology. For over three decades CTC has provided critical liquidity across derivatives exchanges worldwide - making them fairer, more transparent, and more efficient. We strive to be the most innovative firm in the industry today, tomorrow, and long into the future while upholding ethical excellence. We believe that CTC makes a positive impact on the markets, the lives of our employees, and all the communities to which we belong. Started in 1995 by a team of forward-thinking Traders, we are proud to call ourselves an industry leader that keeps making markets and each other better. The Role As a Software Engineering (SE) Intern, you will be challenged to learn and adapt in a dynamic, forward-thinking, and fast-paced environment and will experience life as an engineer on one of our technology teams. Your impact is immediate and meaningful. You will work alongside software engineers designing, solving and testing complex coding problems that will deliver solutions to our trading tools and risk applications. You will build the tools traders and quants rely on to price, trade, and manage risk in real time - and because our release cycles are measured in days, you will watch your code reach production and affect the desk almost immediately. This work draws on a solid foundation in computer science, software development practice, and strong communication and teamwork. You will get exposure to the design and implementation of these systems, which will allow you to build upon your technical skills. As part of the Summer Associate cohort, you will learn and socialize alongside other SE Interns and Quant Trading (QT) Interns. There are a variety of languages you could get practical experience working with including (but

Do you have an insatiable curiosity and passion for science? Do you want to partner with PIs at the top research institutions in the world to accelerate their life's work? We are seeking a dedicated and science-savvy Senior Account Manager to lead our relationships with a few key R1 universities in the Northeastern or Mid-Atlantic regions. NVIDIA’s accelerated computing platform is the scientific instrument transitioning research from traditional sequential computing to massively parallel, neural networks driven labs. Our full-stack platform includes supercomputers, the CUDA programming model, and hundreds of libraries, frameworks, and models. From BioNeMo for structural biology, Parabricks for genomics, Omniverse for 3D virtual worlds, and RAPIDS for data science, to CUDA-Q for hybrid quantum-classical computing, PhysicsNeMo for physics-informed AI, and Earth-2 for climate modeling, we are empowering the next scientific breakthroughs. What You'll Be Doing: Institution and Government Engagement: Serve as a trusted advisor to university and occasionally, state government leaders, communicating NVIDIA's vision, technology roadmaps, and research impact. Grow the Business: Champion organic business growth, forecast revenue, and collaborate with IT and business partners on go-to-market strategies. Research Community Partnership: Forge strong connections with leading research labs and PIs across diverse scientific domains (e.g., AI/ML, life sciences, physical sciences, climate science, engineering, and materials science). You will understand their grand challenges and keep a pulse on emerging computational methods. Strategy Execution: Engage internal cross-functional NVIDIA teams (Solution Architects, Developer Relations, Product Management, Business Units, etc.) and university partners to accelerate science on NVIDIA’s platform. Ecosystem Enablement &

At Gilead, we’re creating a healthier world for all people. For more than 35 years, we’ve tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world’s biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference. Every member of Gilead’s team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we’re looking for the next wave of passionate and ambitious people ready to make a direct impact. We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together. Job Description As the primary Public Affairs liaison to the R&D organization, the V ice P resident will develop and execute an integrated internal and external communications strategy that advances enterprise priorities, strengthens trust in our science, and enables business readiness for key pipeline and regulatory events. This leader will provide senior counsel to R&D and enterprise leaders on reputation and risk, set the operating model and governance for R&D communications globally, and build a high-performing team with deep scientific, regulatory, and issues-management expert

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . Within Pinterest, the Pinterest Labs organization focuses on applied ML research and development. Labs focuses on a broad variety of AI/ML initiatives, such as core computer vision, multimodal representation learning, heterogeneous graph neural networks, generative modeling, recommender systems, etc. This is the group that develops foundation ML models that fully leverage the tens of billions of Pins and the associated knowledge graph to improve the core product. We are currently hiring for the visual modeling team in Labs, which focuses on developing Pinterest Canvas. Canvas is a foundation text-to-image model developed internally for helping various visualization, inpainting, and outpainting products. In this role, you’ll get to work with Pinterest’s rich visual-text dataset to build large-scale generative models which are continuously being s

Overview: The Data Acquisition team within the Foundations organization at OpenAI is responsible for all aspects of data collection to support our model training operations. Our team manages web crawling and GPTBot services and works closely with Data Processing, Architecture, and Scaling teams. We are looking for a skilled Full-Stack Engineer to join our Data Acquisition team to build and optimize the interfaces and tools that power our data infrastructure. Responsibilities: Develop and maintain full-stack applications that support data acquisition, including internal tools and dashboards. Collaborate closely with cross-functional teams, including Data Processing, Architecture, and Scaling, to ensure seamless data ingestion and workflow management. Design and implement APIs to facilitate data interactions between internal services and external data sources. Enhance user experience by developing intuitive web-based interfaces for managing and monitoring data pipelines. Optimize backend services for performance, scalability, and security in a distributed computing environment. Work with legal and compliance teams to ensure our data acquisition processes adhere to privacy regulations and best practices. Deploy and maintain infrastructure using Kubernetes and Infrastructure-as-Code (IaC) methodologies. Analyze system performance, conduct experiments, and improve data workflows to maximize efficiency. Qualifications: BS/MS/PhD in Computer Science or a related field. 4+ years of industry experience in full-stack development. Proficiency in frontend frameworks (React, Vue, or similar) and backend technologies such as Python, Node.js, or Go. Strong expertise in RESTful APIs, GraphQL, and database design (SQL and NoSQL). Experience building data-intensive applications that handle large-scale datasets. Familiarity with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker). Prior experience with web crawling and large-scale data processing is a

About the Team The Interpretability team studies internal representations of deep learning models. We are interested in using representations to understand model behavior, and in engineering models to have more understandable representations. We are particularly interested in applying our understanding to ensure the alignment of powerful AI systems. Our working style is collaborative and curiosity-driven. About the Role OpenAI is seeking a researcher passionate about understanding deep networks, with a strong background in engineering, quantitative reasoning, and the research process. You will develop and carry out a research plan in mechanistic interpretability, in close collaboration with a highly motivated team. You will play a critical role in helping OpenAI ensure future models remain safe even as they grow in capability. This will make a significant impact on our goal of building and deploying safe AGI. In this role, you will: Develop and publish research on techniques for understanding representations of deep networks. Engineer infrastructure for studying model internals at scale. Collaborate across teams to work on projects that OpenAI is uniquely suited to pursue. Guide research directions toward demonstrable usefulness and/or long-term scalability. You might thrive in this role if you: Are excited about OpenAI’s mission of ensuring AGI benefits all of humanity, and are aligned with OpenAI’s charter . Show enthusiasm for long-term AI safety & alignment, and have thought deeply about technical paths to safe AGI. Bring experience in the field of AI safety & alignment, mechanistic interpretability, or spiritually related disciplines. Hold a Ph.D. or have research experience in computer science, machine learning, or a related field. Thrive in environments involving large-scale AI systems, and are excited to make use of OpenAI’s unique resources in this area. Possess 2+ years of research engineering experience and proficiency in Python or similar languag

About the Team OpenAI’s Agentic Data Science team helps shape how AI agents are built, deployed, and improved across our products. We partner with product, engineering, research, and security teams to define meaningful measures of success, understand how our systems behave in the real world, and translate evidence into better decisions. As AI agents become more capable, they can write and execute code, access sensitive systems, and complete increasingly complex tasks with greater autonomy. These capabilities create powerful opportunities to improve cybersecurity, but they also introduce risks that traditional security tools and processes were not designed to address. Meeting this moment requires new ways to measure security, evaluate defenses, and distinguish genuine risk reduction from friction that slows users down. About the Role We are looking for a senior data scientist to help define what effective cybersecurity looks like in the age of AI agents. You will work across OpenAI’s Security organization and cybersecurity product teams to measure emerging risks, improve internal security controls, and shape AI-powered security products. The problems are foundational: How do we know whether an agent’s security controls are effective? Which safeguards meaningfully reduce risk, and which create unnecessary friction? When an AI system identifies a potential vulnerability, how do we determine whether the finding is accurate, actionable, and ultimately resolved? How do we detect anomalous behavior or risky access when the systems themselves are changing rapidly? You will report into Data Science while partnering closely with Security, Cyber Product, Engineering, and Research. This is a high-ownership role for someone who can establish a new analytical discipline, operate across organizational boundaries, and turn ambiguous security challenges into measurable improvements. In This Role You Will Define how we measure AI-agent security. Establish metrics and evaluation frame

About the Team OpenAI’s GTM Data Science team helps shape how our products are adopted, monetized, and scaled across organizations. We work at the intersection of Product, Go-to-Market, Finance, Research, and Data, turning product usage, customer evidence, and market signals into decisions that grow durable enterprise value. We’re looking for a senior Data Scientist to own the analytical strategy for enterprise knowledge-worker adoption. As ChatGPT Work and Codex become capable of research, analysis, document creation, spreadsheets, presentations, internal knowledge synthesis, and other agentic workflows, you will help determine how these products become embedded in everyday work—not merely tried once. About the Role You will define how we measure activation, retained usage, workflow depth, and value across ChatGPT Work, Codex, and connected enterprise systems. You will explain why adoption succeeds or stalls and identify the product, enablement, and commercial interventions most likely to create durable usage. This is a hands-on, zero-to-one role. You will work through imperfect telemetry, overlapping product surfaces, evolving definitions, and ambiguous business questions. You will partner closely with GTM, Product, Finance, Research, Customer Deployment, Analytics Engineering, and Data Science. Your work is successful when it changes a product, GTM, or investment decision. In This Role, You Will Define a trusted measurement framework for knowledge-worker adoption, including identity, eligible populations, activation, retained usage, penetration, workflow depth, feature adoption, and monetization. Map the knowledge-worker journey from initial exposure through first successful task, repeated workflows, multi-surface usage, and durable adoption. Identify which personas, functions, use cases, product capabilities, and account conditions are associated with deep and retained usage. Design and evaluate experiments and quasi-experiments across onboarding, enablement, wo