Ai Research Intern in New York

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

Overview: Guidepoint’s Business Development teams are passionate about expanding our reach with both new and existing clients. We support all Guidepoint’s service offerings, helping to build relationships and communicate how Guidepoint helps clients stay informed and make better business decisions. Our teams are motivated to provide custom offerings designed to help every potential client make the most of their partnership with Guidepoint. The Business Development team is looking for a Sales Development Representative/Business Development Intern to further develop our business with corporations, financial institutions, and consulting firms within the region . The Associate will be responsible for assisting in building a pipeline of prospects across a variety of industries. This is an exciting opportunity for a self-starter who wants to learn more about financial services, consulting, or other industries and make a significant contribution to our business model. This is a hybrid role based in our New York City office starting June 7, 2027. The application deadline for this role is November 1, 2026. What You'll Do: Help create and develop a robust pipeline of qualified prospects Research target industries, sub-sectors and companies in order to understand their information/ research needs and pain-points Map companies in order to identify target groups and job functions Prospect targets via email and telephone and schedule meetings Generate engaging content for meetings with prospects and effectively articulate our value proposition Develop a strong knowledge and understanding of the competitive landscape Provide client/ prospect feedback to sales and management What You Have: Pursuing a Bachelors degree, graduating in 2028 Mature communicator capable of handling high-profile clients Intellectual curiosity and desire to learn Demonstrated ability to work both individually and as a part of a team Ability to think creatively and prioriti

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

Salary range - $250k - $350k | Equity - up to 0.5% | In-person NYC About Datalab Datalab trains models that read documents reliably at scale. The world's most important information is trapped in PDFs, scans, and files that can't easily be parsed, and getting it out correctly matters. From frontier AI labs processing training data to Fortune 500s like Siemens extracting decades of engineering records, Datalab is where businesses turn to when extraction has to be right. We’re at an 8-figure run rate with a team of 7. Anthropic is a customer. And we have hundreds more across FAANG, frontier AI labs, healthcare, finance, government, and legal. Our tools, Chandra, Surya, Marker, and Lift, have 70,000+ GitHub stars and broad developer mindshare. We're backed by founding members of OpenAI, FAIR, and Hugging Face. Role Overview We're looking for a Research Engineer to own problems end to end across our models, inference service, and product. You won't just train a model and hand it off. You'll take it from training through benchmarking, into our inference stack, and work with the team to integrate it into our products. We're a small team that has shipped the current state of the art OCR model, Chandra. Our models collectively have 70k+ Github stars. Our tools are used internally at frontier AI labs like Anthropic, and Fortune 500 enterprises like Siemens. Our team focuses on training small, efficient models that outperform much larger LLMs on domain-specific tasks (like OCR, structured extraction, tables). We move fast, prioritize practical results, and build tools that are open, reproducible, and built to last. You'll test hypotheses quickly, iterate on results, and balance experimental rigor with shipping to customers. Day to day: A typical project might look like: identify a gap in extraction quality on long documents, train and benchmark a new model, optimize it for inference, and work with the team to ship it to users. Concretely: Train and evaluate models: Train task-

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

ROLE SUMMARY The primary purpose of this job role is to function as a member of the Sample Logistics (SL) operations within Pfizer Vaccine Research and Development (VRD). The incumbent will be responsible for receiving samples from global clinical/study trials according to approved protocols and processes. The individual will participate in all aspects of tracking and documenting the chain of custody of samples. The incumbent’s role will include sample receipt, documentation, storage, tracking, aliquoting, distribution to the testing labs, and sample disposal. The colleague will work in a team setting and will share roles and responsibilities as assigned by the team leader/manager. ROLE RESPONSIBILITIES The incumbent will be required to complete all processes as detailed in Sample Management’s Standard Operating Procedures. The colleague will perform job responsibilities in compliance with GXP and all other regulatory agency requirements. The candidate will receive samples from clinical trial sites and enter samples into an electronic database management system. Performs sample storage and retrieval using manual freezers and BiOS (automated freezer storage system). Performs manual sample aliquoting and aliquoting using the Hamilton robotic instrument. Ship samples and lab supplies to external and internal testing labs. Assists in the general maintenance of the Hamilton robotic instruments. Participates in the shipment discrepancy resolution process. Completes documentation according to cGMP/GLP and all other regulatory agency requirements and archives documents as per applicable policies. Carries out sample disposal/sample destruction according to regulated policies and procedures Performs other duties as assigned. QUALIFICATIONS Must Have Bachelor of Science Degree or Bachel

Overview: The Insights product is a new offering for existing Guidepoint clients that offers teleconferences, in-person events and a call transcript library covering a wide of industries/topics that facilitate investment research. All content features experts from Guidepoint’s proprietary global network and is developed by former investment professionals (i.e. private equity/hedge fund) and sell-side equity research analysts. What You'll Do: Monitor a coverage universe of public companies within the technology sector by tracking earnings releases, investor presentations, SEC filings, sell-side research and industry news Create and moderate teleconferences on timely topics featuring an expert from Guidepoint’s network with the goal of producing actionable insights for clients Review teleconference transcripts to ensure quality of content High performers will be considered for full-time employment What You Have: Bachelor’s degree or Master’s degree Minimum 10 years of sell-side or buy-side analyst experience Must follow bottoms-up, fundamental approach to investment research focusing on individual companies Has covered Technology sector (Enterprise Software/SaaS, Internet (FANG etc) and/or Hardware/Semiconductors); candidates must be current on company/industry knowledge Ability to work in a fast-paced entrepreneurial environment Outgoing personality with the ability to speak with people at all professional levels Intellectual curiosity and desire to learn Effective time management and organizational skills Demonstrated ability to work both individually and as part of a team What We Offer: This is a full-time consultant role with the potential to convert to full time. The expected annual salary is $210,000 - $240,000. This rate may vary depending on job-related knowledge, skills, and experience, as well as geographic location. You will also be eligible for the following benefits: Friday happy hour, “Summer Fridays”, and free snacks and bevera

OVERVIEW: The Insights product is an offering for Guidepoint's institutional investment and corporate clients, now in its 9 th year, that offers teleconferences, surveys, in-person events, and AI-facilitated data and content. The teleconferences live in an online library portal, which covers a wide of industries/topics that enables our clients to make informed decisions. All content features experts from Guidepoint’s proprietary global network and is conceptualized and hosted by former investment professionals (i.e. hedge fund analysts), sell-side equity research analysts, and industry professionals. This is a hybrid position based in our New York City office. What you'll do: Build and lead a high-caliber research team: Recruit, develop, and manage a group of analysts producing differentiated, investor-grade insights across the technology, media and telecom (TMT) space, with full ownership over research quality, agenda-setting, and output. Own the research strategy and content roadmap: Define what gets covered and why—shaping expert calls, thematic work, and proprietary content aligned with real-time public equity investor demand Drive business growth and AI-enabled innovation: Partner with commercial and product teams to launch new research offerings, leveraging AI to scale insight generation, enhance workflows, and create differentiated products for institutional investor clients. What you have: Bachelor’s degree or Master’s degree Minimum 10 years of sell-side or buy-side analyst experience Must follow bottoms-up, fundamental approach to investment research focusing on individual companies Has covered all parts of the global TMT sector (Enterprise Software, Hardware & Semis and Consumer Internet); candidates must be current on company/industry knowledge Ability to work in a fast-paced entrepreneurial environment Outgoing personality with the ability to speak with people at all professional levels Intellectual curiosity and desire

We are looking for a Recruiting Sourcer to play a key role in the continued growth and success of Datadog by finding exceptional talent. The Recruiting Sourcer will partner closely with Recruiters to develop creative and tailored strategies to engage passive candidates. You will be the first point of contact for new candidates in the hiring process, playing a critical role in identifying great candidates and telling the Datadog story. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Generate and maintain active and passive candidate pipeline for complex, hard-to-fill positions using various sources such as: LinkedIn Recruiter, Gem, Sourcing Campaigns, Google X Ray searching, previous event attendee lists, etc.. Amplify and champion job openings internally and work with our employee base to find hidden talent in their networks. Develop and implement strategic sourcing plans and track progress throughout the search to be shared with stakeholders Use your own research to get creative on where to find talent that otherwise might stay off the radar including diverse talent Map out the talent market, keeping a pulse of talent flow, market attrition, and which companies employ the talent we are after Specialize in event/project management, messaging analysis, creative prospecting, and stakeholder engagement Who You Are: An established Recruiter or Sourcer with 3+ years of experience working for a search firm or in-house recruiting team for high volume recruiting An individual with a high sense of urgency, strong communication skills and detail-oriented Tenacious and passionate about sourcing and engaging candidates Able to work independently and simultaneously on multiple roles An individual with a proven track record of engaging ta

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: Modal considers high-quality documentation to be essential for developer experience, and we see docs becoming even more important as agents increasingly deploy and operate Modal Apps. We are looking for a content-minded engineer who will partner with our product teams to curate Modal’s technical documentation and maintain a high quality bar across multiple dimensions. Responsibilities: Thinking holistically about content architecture and how the docs should evolve as Modal introduces new products and features Innovating on novel documentation formats and delivery channels to optimize agent productivity, in collaboration with our Agent DX research team Developing content standards, style guides, and automated enforcement mechanisms to ensure consistent style and high quality Building and maintaining automated pipelines that will enforce the correctness of code examp

The mission of The New York Times is to seek the truth and help people understand the world. That means independent journalism is at the heart of all we do as a company. It’s why we have a world-renowned newsroom that sends journalists to report on the ground from nearly 160 countries. It’s why we focus deeply on how our readers will experience our journalism, from print to audio to a world-class digital and app destination. And it’s why our business strategy centers on making journalism so good that it’s worth paying for. About the Role The New York Times' Marketing team is looking for an Associate Manager, Growth Media Strategy (Temporary) oversees paid digital media strategy for our Acquisition program.The Enterprise Paid Acquisition team works on projects in service of enterprise subscription growth, making decisions to ensure we maximize paid subscriptions across products, including the All Access bundle, Cooking, Games and The Athletic. Job Description You will manage campaign activation, development, optimization and reporting. You will work with internal partners in Marketing, Creative, Data Insights, and Audience in helping us reach our goal to achieve 15 million subscribers by 2027. This is a hybrid role based in our New York headquarters, reporting to the Manager, Growth Media Strategy. You can typically expect to be in the office 3 days per week. Responsibilities: You will provide performance reports and insights for ongoing analysis, including ad hoc analysis to determine short and long-term optimizations and support forecasting needs. Use past insights, data visualizations, and research to develop POVs and share media expertise with the immediate and broader team in both written and verbal formats. Manage detailed media budgets, allocations, and delivery across multiple campaigns and vendors to maximize return on ad spend. Partner with the in-house platforms team, guiding campaign execution. Approve optimization recommendations and enhance perfor

About the team OpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development. About the Role We are looking for customer-focused software engineers to build effective custom software that leverages OpenAI’s APIs to solve real customer problems. As an FDSWE, you will work with our customers and OpenAI Forward Deployed Engineers to design and implement scalable solutions that solve their most difficult problems. You will design abstractions to solve customer problems, and then use them to scale our speed and quality of delivery across all Forward Deployed engagements. You will collaborate closely with Sales, Solutions Engineering, Solutions Architects, and Customer Success Managers who work on the same account. You will also work with our Research and Applied Product and Engineering teams to provide insightful customer feedback. This role is based in NYC. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required. In this role, you will: Embed deeply with strategic customers to understand their business challenges and technical requirements in detail. Design, architect, and develop full-stack solutions using an experiment-driven, iterative approach. Prepare detailed scopes of work and project plans for both proof-of-concept prototypes and full production deployments. Work hands-on with customers' technical teams as a technical expert and trusted advisor, coding side-by-side to drive projects to completion on their infrastructure. Collaborate with Product, Research and Applied teams to ensure seamless customer experiences, project success and actionable product feedback Contribute to internal knowledge bases, codifying best practices and sharing insights gained from customer engagements to scale the Forward Deployed Engineering function. You’ll thrive in this role if

Today, everyone from PMs to Sales to SREs uses AI to query their business data - but every answer is a one-off with no governance, no consistency, and no visibility for data teams. We're building the system that fixes this: a context layer that learns from existing data tools driving high quality and consistent answers, an AI-powered query experience, full governance for data teams, and rich analysis surfaces to present and share the results. You'd be building a zero to one product of what we believe will become a major new product line for Datadog. At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What you'll do Drive the product vision and roadmap spanning the Data agent experience, the semantic/context layer, governance console, chat experience and analysis surfaces (dashboards, notebooks, sheets) Own the end-to-end product lifecycle from discovery to launch, working with a cross-functional team of engineers, designers and other PMs Deeply understand the needs of two key personas: data teams (who govern and curate) and data consumers (PMs, engineers, SREs, executives who ask questions) Design the context and governance layer that makes AI-powered analytics trustworthy - including auto-generation from existing BI tools, eval frameworks, confidence scoring, and self-improvement loops Define how observability data and business data come together to serve unique use cases Engage directly with early customers and internal dogfooding users to iterate on accuracy, usability, and trust Independently research the competitive landscape across legacy BI vendors, warehouse-native analytics, AI-first startups, and AI labs Work on the product pricing Work with GTM teams to define positioning, packaging, and the path to displacing entrenched tools Who you are 5+ years of experience