Machine Learning 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

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 . Pinterest is building its programmatic and ad tech capabilities from the ground up to enhance the comprehensiveness and shoppability of ads. By expanding advertising demand sources through strategic partnerships with third-party platforms, agencies, and media companies, we are strengthening our performance marketing ecosystem. As Senior Manager of Programmatic Tech Operations, you will play a pivotal role in shaping the operational strategy, team, and roadmap behind a new and high-impact function. This is a rare opportunity to make a lasting impact on Pinterest’s global programmatic advertising solutions, influence ad products at scale, and drive innovation across one of the internet’s most engaged user communities. What you’ll do: Build and lead Pinterest’s Programmatic Tech Operations, shaping scalable processes, team structure, and best

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 hiring an Enterprise Account Executive to accelerate Modal’s growth with the world’s most innovative AI companies. This is a high-impact role where you’ll own the full sales cycle—from building pipeline to closing large, strategic enterprise deals. You’ll partner directly with our founders, engineering, and product teams to help customers harness Modal’s infrastructure to train, deploy, and scale AI applications. You’ll be expected to operate as a builder: developing new relationships, shaping our GTM motion, and serving as the voice of the customer inside Modal. The ideal candidate is both technically curious and commercially driven—equally comfortable in a room with C-level executives and with machine learning engineers. In this role, you will: Drive new business by generating pipeline, negotiating, and closing complex enterprise deals Build deep, trusted r

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

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We are a visionary and innovative industry expert team with a track record of architecting, building, and taking solutions to market applicable to various sub-verticals across financial services, spanning retail and commercial banking, lending and payments. There is only one Data Cloud. Snowflake's founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. But it didn't stop there. They engineered Snowflake to power the Data Cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. This is our vision: a world with endless insights to tackle the challenges and opportunities of today and reveal the possibilities of tomorrow. Are you passionate about taking new ideas and innovation to market? Have you successfully applied technology to solve business problems in core banking? If so, we have a strategic opportunity to use your expertise across Data, Cloud, and AI to drive transformational changes across the financial services industry in a highly visible role collaborating with internal and external leadership. Snowflake is seeking a seasoned Financial Services Industry Architect with deep, hands-on experience designing and d

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, Washington D.C., London and Amsterdam. The FinOps function is responsible for financial accountability, visibility, and optimization across all engineering-related spend at Plaid. This includes cloud infrastructure, AI/ML and data workloads, third-party SaaS tools, and other technical investments that support Plaid’s products and internal platforms. The team operates at the intersection of Engineering, Product, and Finance, ensuring that spending decisions are transparent, intentional, and aligned with product strategy and business priorities. Rather than functioning as a cost-control or approval layer, FinOps enables teams to understand, own, and optimize their spend while maintaining engineering velocity. Responsibilities Monitors and analyzes engineering spend across cloud, AI/ML, data platforms, and SaaS, identifying trends, anomalies, and optimization opportunities. Builds and maintains forecasts for engineering spend, partnering with Finance and engineering leaders to understand drivers, assumptions, and risks. Partners with engineering, product, and TPMs to incorporate cost considerations into roadmaps, architectural decisions, and execution plans. Leads cost optimization initiatives, such as rightsizing, commitment strategies, an

ROLE SUMMARY We are looking for a Senior Data Scientist to lead complex data science engagements that combine traditional statistical modelling with Generative AI. You will work hands-on with very large datasets across disparate systems and formats, translate ambiguous business problems into rigorous analytical solutions, and present those solutions clearly to C-level stakeholders. This is a delivery-first role with a fast track into technical leadership: alongside your own project work, you will help guide junior data scientists and shape how Lynx builds and ships data science solutions. KEY RESPONSIBILITIES Solution Design & Delivery Design and deliver end-to-end solutions for defined data science problems, combining classical modelling, data transformation, and Generative AI / LLM techniques. Work hands-on with very large datasets across disparate stores and formats, from ingestion and transformation through to modelling and validation. Apply statistical and machine learning methods to business problems such as customer retention, campaign management, and commercial performance optimisation. Client Communication & Leadership Present results and prepare client-ready materials for project stakeholders, including C-level audiences, translating technical work into clear business narratives. Lead smaller data science workstreams, with support from internal leadership and the PMO, including day-to-day guidance for junior team members. Partner with delivery and account teams to scope problems, set realistic timelines, and manage stakeholder expectations. Knowledge Building Create reusable documentation, presentations, and code libraries during projects so future engagements can build on prior work. Participate in internal education, research, and knowledge-sharing initiatives that raise the technical bar across the practice. SKILLS, QUALIFICATIONS AND EXPERIENCE 8+ years of overall experience in data science, with a track record of leading analytic

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. We are the Fraud Data team within Plaid's Fraud organization. We leverage relationships and activity across Plaid’s network to develop machine learning models that help customers detect and prevent fraud while minimizing friction for legitimate users. Our team owns the full model lifecycle, from data processing and experimentation to feature pipelines, model serving, and monitoring. In this research role, you’ll partner closely with Machine Learning Engineers and Data Scientists to uncover new signals, explore and evaluate innovative modeling approaches, and translate promising research into production-ready solutions that strengthen fraud detection across Plaid’s network. As a Senior Research Scientist, you will lead applied research to develop next-generation fraud detection models across relational graphs, sequential events, images, and video data. You’ll design rigorous experiments and evaluation methodologies that reflect real-world fraud dynamics, while exploring state-of-the-art approaches such as Graph Neural Networks and Transformer-based foundation models. In close partnership with Machine Learning Engineers, you’ll translate promising research into production-ready solu

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. The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. The team owns the full ML lifecycle—from feature pipelines and model training to production serving and monitoring—building reliable, scalable systems that deliver high-quality fraud detection as we grow to support hundreds of customers. As a Senior Machine Learning Engineer, you will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale. You’ll build robust observability, monitoring, and automated debugging capabilities, while leveraging AI-assisted tools to investigate complex system behavior and maintain high reliability. You’ll partner closely with ML Infrastructure, Data Science, and Product teams to execute critical technical initiatives and deliver scalable, high-impact ML solutions. Responsibilities: Build and scale machine learning systems that power a rapidly growing fraud detection product in a fast-paced environment. Solve complex technical challenges at the intersect

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 Embedded Insights team supports Plaid’s mission to build a world-class suite of intelligence products. We identify the best opportunities to use machine learning in Plaid products, prove out those opportunities, and collaborate with cross-functional partners to turn them into real-world production systems. About the Role As a Senior Machine Learning Engineer on Embedded Insights, you will help shape Plaid’s future by building machine learning-powered products and features. You will initially support the Plaid App, working on a 0-to-1 consumer-facing product and helping establish product-market fit for a new business line. You will work closely with product managers, data scientists, engineers, customers, and other machine learning engineers to translate ambiguous opportunities into effective ML systems that create measurable customer value. In supporting the Plaid App, you will: Build machine learning-based features for a 0-to-1 consumer-facing product. Partner with product managers to translate ambiguous business requirements into machine learning problems and influence product strategy and roadmap decisions. Rapidly iterate and experiment to help drive product

CLEAR is building THE secure identity company of the future. Our mission is to make experiences safer and easier—physically and digitally. With more than 43 million Members and a growing network of partners across the world, CLEAR's secure identity platform is transforming the way people live, work, and travel. Whether it’s at the airport, stadium, or throughout your everyday life, CLEAR unlocks the magic of frictionless experiences. We’re looking for an experienced Machine Learning Engineer to help us build the next generation of products which will go beyond just ID and enable our members to leverage the power of a networked digital identity. As a Machine Learning Engineer at CLEAR, you will participate in the design, implementation, testing, and deployment of applications to build and enhance our platform - one that interconnects dozens of attributes and qualifications while keeping member privacy and security at the core. A brief highlight of our tech stack: Python / Postgres / Snowflake / dbt AWS SageMaker and MLflow What you'll do: Own and drive the foundational work of a ML system at CLEAR Design, build and deploy ML models for various applications, such as document and image processing, fraud detection. Develop and implement robust data pipelines at a variety of scales, including collection, pre-processing, transformation, and feature engineering Partner with product and other stakeholders to uncover requirements, to innovate, and to solve complex problems Have a strong sense of ownership, responsible for architectural decision-making and striving for continuous improvement in technology and processes at CLEAR What you're great at: 3+ years of experience building, operating and scaling ML models for consumer applications, particularly those with experience building end-to-end systems Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance Expertise in best practices for feature enginee

Applied AI is where Datadog's ambitious AI bets get built and shipped ( Bits Chat , updog ). We sit at the intersection of research and product: turning promising capabilities from Datadog AI Research lab and the research community into production systems that reach real customers. The team builds the foundations for agentic systems capable of operating at scale in complex production environments. Current bets span agents that run autonomously at scale, context and memory layers that make those agents more intelligent over time, and tools that help customers build and validate AI-native services in production. The mandate is to move fast from idea to customer impact, and when a product finds its footing, to set it up for growth. As an Engineering Manager I in Applied AI, you will lead a team of engineers and applied scientists working on one of these challenges. You will define technical direction, run short feedback loops, make deliberate decisions about what to pursue or stop, and work closely with product managers, research teams, and cross-functional partners to ship AI capabilities that matter. At Datadog, we place value in our office culture, the relationships and collaboration it builds and the creativity it brings. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do Lead and develop a team of engineers and applied scientists focused on building the foundations for agents operating at scale Work closely with product managers, research teams, and cross-functional partners to shape the team's bets from initial framing through to broader adoption, with a clear definition of success criteria at each stage Own end-to-end delivery of high-quality AI systems, from early research exploration to production-grade reliability, with high standards for operational excellence, system reliability, and technical quality Navigate the unique challenges of shipping AI-powered products: balancing quali

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, Washington D.C., London and Amsterdam. We believe Plaid has the power to be the next-gen Credit Bureau - supporting large scale adoption of cash flow into the credit underwriting process. The Credit Decisioning platform team is responsible for building best-in-class cashflow based insights products that enable lenders to make more holistic lending decisions and empower broader access to Credit products for prospective borrowers. We own the systems and tooling that form the platform to build and serve these insights at huge scale, partnering with our Data partners to release new products yearly. You will be defining the future architecture of Credit insights products and executing against an ambitious product roadmap. You will partner with our Product, Data Science, and Machine Learning team to iterate on and productionize new insights that enable our customers to make more holistic lending decisions. Responsibilities: Leading technical architecture and execution across credit insights products: everything from data fetching and online feature serving for API requests, to offline production pipelines and tooling for model training. Scaling and evolving the architecture through an expected ~100x increase in load from deterministic factors

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, Washington D.C., London and Amsterdam. The Financial Management team develops market leading API products that power the most influential digital finance experiences that help millions of consumers and small businesses every day - think budgeting apps and financial management tools offered by banks and wealth platforms. Our mission is to unlock financial freedom for everyone by making it easier for customers and businesses to meet their financial goals. We do this through a state-of-the art, data aggregation and machine learning engine that enables our customers to seamlessly build delightful experiences on top of consumer-permissioned financial data. Every day, Plaid helps thousands of developers build a better financial future. You'll be responsible for defining the products which enable Plaid and our customers to shape the future of financial services. We are looking for the right product manager to own and grow Plaid’s financial management products, including Transactions, Investments, and Liabilities, even more valuable to our customers and partners. Plaid is on a journey to build insights on top of open banking data that drive value for customers, consumers and the fintech ecosystem in general. Additionally AI is changing how consu