Agent Post Training in New York

As a Research Scientist on our team, you will partner with Research Engineers, working on fundamental research problems and collaborating with Datadog's product and engineering teams to translate research advances into products. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure Design and build simulated environments and RL training loops for on-policy agent training and evaluation Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products Stay at the forefront of foundation models, world models, and RL-based agent research Contribute to r

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

Datadog’s Technical Solutions organization includes 1,200+ sales engineers, support engineers, post-sales experts, and solution architects. They run on an ecosystem of enterprise platforms and internal tools that directly shape how we serve customers. Technical Solutions Operations (“TSO”) owns that ecosystem. We manage the full lifecycle of the systems TS depends on: Zendesk, Jira, Confluence, and a growing portfolio of off-the-shelf and purpose-built tools. We do the work to operate, maintain, and evolve the platforms powering daily workflows across TS. When a vendor tool reaches its limits, we extend it through customization, integration, or targeted solution development, tapping internal partners across Datadog as needed. We’re looking for a Systems Engineer who wants to own enterprise platforms end-to-end. You go from understanding the business process, to designing the right solution (whether that’s configuration, integration, or code), to measuring whether it actually moved the needle. 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: Own enterprise systems through their full lifecycle. You’ll be the technical owner for one or more platforms that TS relies on daily. That means understanding how the system is used, where it’s falling short, what’s coming from the vendor roadmap, and what needs to change. You drive improvements from assessment through implementation. Engineer solutions that create leverage. Not every problem is solved by configuration. You’ll build and evolve enterprise systems, integrations, automations, and internal tools that multiply the effectiveness of 1,200+ technical experts. Where AI can make a solution smarter (e.g., intelligent routing, automated triage, agent-assisted workflows), you'll include AI in the initial design, no

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 like Anthropic to Fortune 500s like Siemens, Datalab is where businesses turn when extraction has to be right. We hit an 8-figure run rate with a team of 7. We have hundreds of customers across FAANG, frontier AI labs, healthcare, finance, government, and legal. Our tools - chandra, surya, marker, and lift - have 70,000+ GitHub stars, millions of monthly downloads, and broad developer mindshare. We’re backed by founding members of OpenAI, FAIR, and Hugging Face. Role Overview Marketing at Datalab already has real infrastructure behind it. We run a combined launch and content calendar with a defined playbook - channels, owners, and cadence for every release - and we ship two to three launches a week against it. No one here works on marketing full-time, so nobody owns the larger question of how we show up when a developer, a buyer, or an AI agent goes looking for a document parser. You’d be our first dedicated marketing hire. You’d inherit a working system rather than a blank page, and the job is to make it run reliably, then make it considerably more ambitious. The natural place to build from is our open source work, which reaches a wide audience and currently does less for us than it could. This is a hands-on role. On a team of 7, you’ll write the blog post, draft the tweets, restructure the docs page, brief the agency if we hire one, and pull the numbers yourself. We’re looking for someone who has done this work somewhere it was done well, and who wants to keep doing it rather than direct someone else who does. There is strong potential for this to grow into a leadership role as we hire more people into the marketing team. An increasing share of our buyers never touch a search results page; they ask a model. We want someone who t

As the Senior Product Manager for the Actions & Automations team, you will own the ecosystem that enables customers, partners, and Datadog teams to build, deploy, and operate AI agents on Datadog. You will drive the strategy and execution for the platform capabilities, developer experience, integrations, and extensibility model that make Datadog the best place to build agents that understand and act on production systems. Modern engineering organizations are entering a new era where software is not only monitored and operated by humans, but increasingly by AI-powered agents. As agentic workflows reshape how teams build, operate, secure, and troubleshoot systems, customers need a platform for creating specialized agents, connecting them to business and engineering systems, governing their behavior, and extending them to solve unique organizational problems. You will define and build the ecosystem that makes this possible. Agent Builder sits at the intersection of Datadog's products, AI capabilities, and ecosystem strategy. You will have the opportunity to work across the breadth of the Datadog platform, partner with teams throughout the company, and help establish Datadog as the foundation for operational AI. 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: Define the vision, strategy, and roadmap for Datadog's Agent Builder platform and ecosystem. Own the core platform capabilities that enable customers and partners to create, customize, deploy, and manage AI agents. Drive the extensibility model for agents, including integrations, tools, actions, context sources, APIs, SDKs, and developer workflows. Shape how agents perform actions across Datadog products and third-party systems. Partner closely with AI, platform, infrastructure, and product tea

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

Datadog's integrations are the connective tissue between our platform and the technologies our customers run in the real world. As a Sr. PM on the Agent Integrations team, you will own the vision, prioritization, and execution for 100+ integrations that run directly inside the Datadog Agent from foundational infrastructure (MySQL, Kafka, Kubernetes) to the rapidly growing landscape of self-hosted AI and on-premise enterprise technologies. This is a high-impact, breadth-first role at the intersection of infrastructure observability and the frontier of AI-native workloads. At Datadog, we place value in our office culture; the relationships it builds, the creativity it brings, and the collaboration of being together. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do: Own the Agent Integrations roadmap. Determine which new integrations to build and which existing ones to improve, balancing customer demand, business impact, and engineering capacity across a catalog of 100+ technologies. Drive the expanding AI integration surface. Lead product strategy for self-hosted AI workloads, including LLM inference frameworks (e.g., Hugging Face TGI, BentoML), AI agents, MCP servers, and model orchestration tools, so Datadog customers can monitor every layer of their AI stack. Expand on-prem and hybrid coverage. Prioritize and execute new integrations for on-prem technologies including storage systems, HPC schedulers, network devices, and legacy enterprise platforms where customers run critical workloads. Build observability for ERP systems. Define and drive Datadog's strategy for monitoring enterprise ERP platforms (SAP, Oracle EBS/Fusion, Microsoft Dynamics) covering performance, job execution health, and integration layer telemetry so enterprise customers can observe their ERP stack alongside the rest of their infrastructure. Analyze adoption and customer feedback at scale. Use data from multiple sources to

Why join us Brex is the intelligent finance platform that enables companies to spend smarter and move faster in more than 200 markets. By combining global corporate cards and banking with intuitive spend management, bill pay, and travel software, Brex enables founders and finance teams to accelerate operations, gain real-time visibility, and control spend effortlessly. Brex’s AI-native automation and world-class service eliminate manual expense and accounting tasks for customers so they can focus on what matters most. Tens of thousands of the world's best companies run on Brex, including DoorDash, Coinbase, Robinhood, Zoom, Plaid, Reddit, and SeatGeek. Working at Brex allows you to push your limits, challenge the status quo, and collaborate with some of the brightest minds in the industry. We’re committed to building a diverse team and inclusive culture and believe your potential should only be limited by how big you can dream. We make this a reality by empowering you with the tools, resources, and support you need to grow your career. Engineering at Brex Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders. What you’ll do We're building AI agents to automate and augment internal functions at Brex, and we're looking for a hands-on builder to help us do it. You'll embed with teams across the company to understand how they actually work, then design, build, and ship agents that deliver real outcomes. You'll work on top of existing models and frameworks, wiring together tools, MCPs, and internal systems into agentic workflows. This is a technical role where you'll spend most of your time building, shipping, and iterating alongside the p

Business Systems drives efficiency across Datadog through business process analysis, systems automation and integrations, AI agent and MCP development, and vendor/software review. The team is increasingly embedded in cross-functional initiatives across People, Finance, GTM , Legal, Recruiting, and Technical Solutions — translating ambiguous business problems into scoped, buildable solutions and owning delivery end-to-end. This is not a generalist BSA role. Each Senior BSA will own a cluster of business functions end-to-end, acting as an internal product manager for their domain rather than processing inbound requests reactively. There are two openings, each covering a different domain: People, Recruiting, Legal or Finance, GTM, Procurement As the role evolves alongside AI, Senior BSAs leverage agentic tools for data aggregation and context gathering while remaining the critical human-in-the-loop layer — owning business context and product outlook, validating use cases, managing stakeholder relationships, and making the judgment calls agents can't. 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: Discovery and scoping: Translate ambiguous asks from business stakeholders into well-defined requirements that Business Systems Engineers can build against. Many stakeholders don't know what they want or the cross-functional impact of what they're asking for — you surface both before engineering begins. Cross-functional visibility: Identify dependencies, downstream impacts, and integration considerations that requesting teams miss. Proactive opportunity identification: Develop deep domain knowledge to identify automation and AI opportunities before they become inbound requests, shifting the team from reactive in

We are investing in agentic AI and need a Senior AI Engineer to lead the design and delivery of these systems. This is a foundational hire: you will own both the agent-facing workstreams — pipelines, orchestration, conversational interfaces — and the underlying context layer that makes them reliable, including memory management, knowledge graph integration, and retrieval infrastructure. You will work closely with data engineers, project leads, and client stakeholders, and play a key role in shaping how Lynx builds and ships AI solutions at scale. What This Involves: Lead the architecture and delivery of agentic AI systems end-to-end: agents, orchestration, tool use, and multi-step reasoning workflows. Own the context layer: design and implement memory architectures (episodic, semantic, working memory) and integrate GraphRAG and knowledge graph retrieval into agentic pipelines. Build robust RAG systems — including vector retrieval, graph traversal, and hybrid search — and ensure retrieval quality through evaluation frameworks. Translate client requirements into technical designs, presenting approaches and trade-offs to both technical and non-technical stakeholders. Define standards and reusable patterns for agentic AI development that other engineers at Lynx can build on. Set up observability, evaluation, and monitoring pipelines to ensure AI systems perform correctly in production. Requirements: 5–8 years of software or ML engineering experience, with at least 2–3 years building LLM-based or agentic AI systems in production. Deep hands-on experience with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or similar) and LLM APIs (OpenAI, Anthropic, etc.). Strong understanding of agent design patterns: ReAct, planning loops, tool use, multi-agent coordination, and memory architectures. Practical experience with GraphRAG or knowledge graph-based retrieval (e.g., Neo4j, Microsoft GraphRAG) and vector databases (Pinecone, Weaviate, Qdrant, etc.). Proficiency in

Code Intelligence sits at the intersection of agentic engineering and product, running agents against real customer source code in production. This is a chance to own an AI-native team at the center of a business-critical growth area. High visibility, high leverage, and squarely in the fastest-growing part of the AI agent space. 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 employees can create a work-life harmony that best fits them. What You'll Do: Lead and develop an existing engineering team, building trust, setting technical direction, and establishing a high bar for ownership and execution. Own the roadmap and execution across four areas: source code indexing, PR Agentic Reviewers (agents that generate artifacts like metric descriptions on customer pull requests), shift-left work (extending code-writing agents to make direct changes to customer repos), and closed-loop feedback and evaluation systems including LLM-as-judge. Set the team's product strategy independently while partnering day-to-day with product teams company-wide — this team functions as its own product org. Build and operate scaled LLM and agent software in production: LLM APIs, agent frameworks, harness and tool-use layers, prompt and eval tooling, and closed-loop evaluation systems that measure and improve agent output quality. Partner closely with customers and internal stakeholders to deeply understand needs and translate them into technical direction. Drive the team's AI-native development practices, setting high standards for safety, validation, and increasing agent autonomy over time. Who You Are: Experienced engineering manager with a track record of shipping products with direct customer interaction, not just internal stakeholders. Hands-on experience building scaled LLM software: LLM APIs, agent frameworks, prompt and eval tooling, h

About the Team API Frontiers turns OpenAI’s frontier models into production APIs that developers can use to build reliable products and agents. We own the core path connecting models to developers through the Responses API, with a focus on safety, reliability, and speed. Working closely with Research, Safety, Codex, and other API teams, we bring new model capabilities into production and improve them through developer feedback. About the Role We are looking for a backend software engineer to build and operate the services behind the Responses API. You will shape API behavior, bring new capabilities from research into production, and make long-running agent workflows dependable and fast. The work combines distributed systems engineering with product judgment: designing useful developer interfaces, managing staged rollouts, and following production issues through to durable fixes. In this role, you will: Design, build, and operate APIs and backend services that bring frontier model capabilities to developers. Partner with Research, Safety, Codex, and API teams to define API behavior and deliver safe, staged launches. Build API capabilities for agent workflows, including task delegation, context sharing, and parallel execution. Strengthen long-running request reliability across timeouts, cancellation, streaming, and background execution. Improve request-processing performance and tail latency through profiling, efficient systems code, and persistent connections. Turn developer feedback and production failures into better observability, diagnostics, and lasting product improvements. Your background might look something like: 5+ years of experience building and operating backend services or developer-facing APIs in production. Strong software engineering fundamentals, with practical knowledge of distributed systems, concurrency, and asynchronous execution. Ability to diagnose production failures and performance bottlenecks using observability data and profiling. Product

The Dashboards product is Datadog's unified single-pane-of-glass for metrics, logs, and traces—a comprehensive treasure trove of observability data. We are transforming Dashboards into an AI-native control surface and the central hub where every team moves seamlessly from question to insight to action – providing a guided experience that feels like having an expert SRE at your side and ensuring the entry point is never an empty canvas. We're hiring a Staff Applied Scientist to define and guarantee the quality of this AI system at scale. "Good" isn't one number — it spans answer quality, tool-selection accuracy (critical given the growing catalog of data sources and visualizations), retrieval relevance, latency, token cost, and end-to-end agent success. The space is full of open questions. How do you evaluate an agent end-to-end when the trajectory is non-deterministic? How do you score tool selection when a user’s query can result in the agent making decisions against dozens of visualizations and data sources – both of which are growing month over month? How do you build a measurement system that catches regressions across all widget types and data sources (e.g., enforcing correct grouping, sorting, and time overrides), and is easy to use and extend by dozens of teams? If those are the problems you want to spend your time on, come build this with us. 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: Own the evaluation strategy for Dashboards, as well as sister teams within our organization. Define the metrics — offline and online, quality and cost, single-turn and multi-turn — that the team and the broader organization optimize against. Build the eval datasets, golden traces, and regression harnesses that catch quality changes before they hit customers, an

Team description At Datadog, AI agents are becoming first-class consumers of observability, security, and software delivery data — from third-party coding agents like Claude Code, Cursor, and Copilot, to our own Bits SRE, Bits Assistant, and Bits Dev Agent. The Agentic Interfaces team owns the platform that connects these agents to Datadog: the MCP Server, the tools and retrieval surfaces agents call into, and — critically — the evaluation systems that tell us whether an agent's experience on Datadog data is actually getting better over time. This role is about that last piece. We're hiring a Staff Applied Scientist to define what "good" means for an Agentic interface at Datadog and to build the measurement systems that make it true. "Good" isn't one number — it spans answer quality, tool-selection accuracy, retrieval relevance, latency, token cost, and end-to-end agent success on real customer workflows. You'll design the evals, build the datasets, define the metrics, and partner with the AI engineers on the team to land the platform that lets every product group at Datadog ship integrations that are demonstrably better release over release. The space is full of open research questions. How do you evaluate an agent end-to-end when the trajectory is non-deterministic? How do you score tool selection when the tool catalog has hundreds of entries and grows weekly? How do you build a measurement system that catches regressions across first-party and third-party agents at once, without each team writing their own harness? If those are the problems you want to spend your time on, come build this with us. Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply. What You’ll Do: Own the evaluation strategy for Datadog's AI agent integrations. Define the metrics — offline and online, quali

InMobi (Corporate) InMobi Group is a global technology company shaping the future of agentic commerce and advertising. Through its ecosystem of businesses — including InMobi Advertising and flagship consumer platform Glance — InMobi leverages data, machine learning, and generative AI to help brands reach audiences more precisely and consumers discover products more intuitively. Glance, which is pioneering new models of agentic commerce, is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. InMobi Advertising InMobi Advertising, part of global technology company InMobi, is an agentic advertising platform helping brands and merchants achieve their business outcomes. Through its proprietary intelligence, AI-led solutions, and vast consumer reach — including flagship consumer platform Glance — InMobi Advertising delivers the omnichannel performance defining what's next in advertising and commerce. Glance is owned and operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of InMobi Pte. Ltd. To learn more, visit advertising.inmobi.com. Glance Glance is an intelligent shopping agent, redefining the commerce experience. Powered by proprietary agentic intelligence and generative AI, Glance delivers a hyper-personalized consumer experience across mobile and TV — shaping the new era of shopping. Glance is operated by Glance InMobi Pte. Ltd., a non-consolidated subsidiary of global technology leader InMobi, and is backed by Mithril Capital, Google and Jio Platforms. To learn more, visit glance.com. Overview of the Role: The Global Enterprise Marketing team operates across brands, channels, events, and geographies, and moves fast. We're looking for a sharp, organized Marketing Events & Operations Coordinator to be the connective tissue that keeps it all running during our highest-stakes, most visible moments. This role reports to the Head of Experiential Marketing and supports the broader cross-functional enterprise