Applied Science Intern

AI Platform builds the foundations of Datadog's AI efforts. The org is 70+ people organised in three pillars: training and serving (GPU clusters, distributed training, low-level infrastructure), agents (agent harnesses, memory systems, the internal AI gateway that routes every LLM request at Datadog), and evaluation and experimentation. This role sits in the evaluation and experimentation pillar, which owns Datadog's shared annotation and evaluation infrastructure — including the evaluation scenario store and the telemetry archival systems used across the Bits org. Together they let an agent travel back in time and query what Datadog looked like at the exact moment an incident happened, so scenarios can be replayed and agent performance tracked over time. Specifically, you'll be the first applied scientist on GenSim (Generative Simulations), the team that builds the environments Datadog's agents learn in. GenSim doesn't replay sampled telemetry — it stands up real, fully instrumented applications that talk to Datadog, drives them with representative traffic, injects controlled failures, and records what happens. Because GenSim injected the failure, it knows the ground truth. That corpus — hundreds of postmortem-derived scenarios and thousands of runnable applications — is today the primary source of post-training data for Datadog's own SRE model, and the substrate that Bits AI SRE and our other agents are trained and evaluated against. The team has no applied science support today and is learning post-training data methodology on the fly. That's the gap this role fills, and the open questions are the interesting part. How do you tell whether a generated environment is actually representative of the messy, incomplete telemetry real customers run — rather than a suspiciously clean one where every monitor exists and every service emits complete logs? How do you make injected problems genuinely hard, and how do you even measure difficulty? How do you define and control

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 performance You are excited to work on

The Behavior AI team builds the AI-based anomaly detection behind Datadog's security products. Our models learn what normal looks like across the billions of logs, events, and telemetry records flowing through the platform every second, and they flag the behavior that does not fit, on every record, in real time, at a cost that makes sense at our scale. What we build does not ship to a single feature. The same models power detection across many of Datadog's security products at once, so the work has impact well beyond any one team. Large general-purpose models are too slow and too expensive to run in that path, so we take the opposite approach: small, custom models, designed for high-throughput stream processing and optimized to run cheaply on every record. We are hiring a Senior Applied Scientist to build these models from start to finish. You will contribute to designing the architecture, training at scale, and the optimization work that takes a model from training to running efficiently on production traffic. This optimization requires a deep understanding of the constraints imposed by both the software and the hardware, together with the applied mathematics to work within them: often it comes down to finding a mathematical reformulation that fits those constraints better, and that judgment can decide whether a model reaches production at all. The work involves a number of open questions. How do you obtain most of the quality of a large model from one that is far smaller and cheap enough to run on the full stream? Where is it worth trading exactness for speed, and how do you reason about the error you accept? How do you make a small model's outputs clear enough that the detection engineers and analysts who rely on it can trust what it reports? If these are the problems you want to work on, we would like to hear from you. At Datadog, we place value in our office culture: the relationships and collaboration it builds, and the creativity it bring

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. À propos d’Anthropic La mission d’Anthropic est de créer des systèmes d’IA fiables, interprétables et orientables. Nous voulons que l'IA soit sûre et bénéfique pour nos utilisateurs et pour la société dans son ensemble. Notre équipe est composée de chercheurs, d'ingénieurs, d'experts politiques et de chefs d'entreprise engagés qui travaillent ensemble pour créer des systèmes d'IA bénéfiques. À propos du poste En tant qu'architecte des solutions partenaires au sein de l'équipe d'IA appliquée d'Anthropic, vous serez architecte avant-vente chargé d'entretenir des relations techniques avec nos intégrateurs de systèmes mondiaux et régionaux (GSI/RSI) et nos partenaires cloud (AWS et GCP). Vous renforcerez nos relations avec les partenaires clés pour accélérer les revenus indirects, permettre leurs pratiques d'IA et exécuter la stratégie GTM à long terme. Responsabilités: Partenariat technique stratégique : soyez un partenaire de réflexion technique pour l'équipe des partenariats Anthropic GTM, en apportant une expertise technique pour mieux comprendre l'environnement des partenaires, en pilotant des programmes stratégiques clés et en identifiant des opportunités pour approfondir leurs compétences techniques. Vous vous intégrez aux équipes techniques de GSI et des partenaires cloud pour mettre en œuvre leurs pratiques en matière d'IA, vous aider à résoudre les problèmes, promouvoir Anthropic au sein de leurs communautés de développeurs et servir de point d'accès pour les problèmes techniques complexes. Développement conjoint de solutions : Collaborer avec des partenaires pour identifier des applications GenAI spécifiques à grande

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. À propos du poste En tant qu'ingénieur·e en IA appliquée au sein de notre équipe Entreprise, votre rôle est de guider certaines des plus grandes entreprises de France vers la frontière de l'IA et de les aider à déployer Claude dans leurs opérations, leurs produits et leurs méthodes de travail. Vous deviendrez le·la spécialiste incontournable de la plateforme Claude et le·la conseiller·ère technique de confiance de vos clients, en conseillant leurs équipes d'ingénierie sur l'architecture, la conception d'agents et l'évaluation, et en travaillant avec les dirigeants et les cadres supérieurs qui sponsorisent ces programmes pour démontrer la valeur ajoutée de Claude et les aider à prendre une décision éclairée quant à l'adoption de Claude. Les transactions avec les entreprises sont différentes. Les cycles d'approvisionnement sont plus longs, davantage de parties prenantes sont impliquées, et la réussite dépend de la capacité à relier le travail technique aux résultats qui importent pour le conseil d'administration. Vous serez tout aussi à l'aise pour conseiller une équipe d'ingénierie sur l'évaluation et l'architecture que pour présenter un dossier de valeur à un CIO ou un CTO, et vous porterez le fil technique à travers chaque étape d'un engagement s'étalant sur plusieurs trimestres. Une grande partie de ce travail se déroule en français, avec des cadres dirigeants et des équipes d'ingénierie françaises, c'est pourquoi un français natif ou courant est essentiel pour ce poste. Vous travaillerez en étroite collaboration avec les responsables de comptes Entreprise et les architectes IA appliquée pour accompagner chaque client depuis la prem

About the team The Applied AI Engineering team works closely with frontier startups. We are trusted advisors to, and thought partners with, startups to ensure that OpenAI’s technology is deployed safely and effectively, whilst also partnering with engineering, research, and product to turn those insights into evaluation systems, product improvements, and better model behavior. This team sits at the intersection of customer reality and model quality. We combine hands-on technical depth with strong product judgment, helping translate complex, high-value use cases into clear signals that can improve both the customer experience and the underlying systems. About the Role As an Applied AI Engineer focused on Codex for startups, you will help developers and founders transform how they build and ship software. You’ll combine hands-on engineering, startup experience, and compelling technical communication to help teams turn the potential of AI coding tools into practical, effective workflows. Presentations and community engagement are central to this role. You will engage developers and founders through technical talks, live demos, workshops, and programmes across the startup ecosystem, alongside targeted work with individual customers. You’ll build reusable tools and resources that extend your impact, and bring insights from developers back to OpenAI’s Product, Research, and Engineering teams. This role is well suited to someone who enjoys both hands-on building and sharing what they learn with others. You should be as excited to lead a room of developers, deliver an engaging presentation, and build relationships across the startup community as you are to explore a technical problem in code—with a focus on sustained adoption and meaningful improvements in how startups work. In this role, you will: Partner with top startups to identify where Codex can have the greatest impact on how their teams build and ship software. Design and lead technical talks, live demos, workshops,