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 a user-facing product   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.   

As a Senior Security Engineer within Application Security at Datadog, you will help secure emerging application architectures where established security patterns may not yet exist, including agentic applications, LLM-enabled products, protocol-driven tool use, and untrusted execution environments. You will establish and validate security models, requirements, and controls while partnering with product and platform teams to develop secure-by-default approaches. Your work will address evolving trust boundaries, agent identity, data access, tool permissions, and runtime behavior through threat modeling, security solutions, and practical implementation. You will have the opportunity to shape how Datadog safely supports new application paradigms and address emerging security risks before they become systemic. 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 security models, requirements, and guardrails for emerging application architectures where established patterns are incomplete or do not yet exist. Design, implement, and validate practical security controls that reduce risk in emerging architectures and high-risk system behaviors. Shape how emerging systems authenticate, authorize actions, isolate workloads, and access data, applying least-privilege principles throughout. Partner with product, platform, Infrastructure Security, and other engineering teams to identify security gaps and embed secure-by-default requirements from early design through production. Lead threat modeling and security assessments for new and existing products, focusing on evolving threats, unfamiliar trust boundaries, and new interaction patterns. Prioritize and drive remediation of emerging security risks, including continuous security testing where it provides durable coverage, and refine security approaches as architectures and threats evolve. Who You Are: You have 5+ years of experience in software engineering or development in a collaborative environment, with Go, Rust, or Python experience preferred. You have experience in security and/or infrastructure/platform engineering, with exposure to distributed systems, application platforms, or agent-based architectures. You have designed or implemented security controls within application infrastructure, such as authentication, authorization, API security, zero-trust networking, workload protection, sandboxing, or runtime hardening. You understand common application security vulnerabilities and mitigation techniques, including those relevant to LLM applications and agentic systems; familiarity with LLM frameworks and protocols such as MCP or A2A is a plus. You bring strong problem-solving skills and can work independently and collaboratively in ambiguous or rapidly evolving problem spaces. You have successfully driven security initiatives with engineering and leadership stakeholders and are committed to staying current with security practices, emerging technologies, and evolving threats. Datadog values people from all walks of life. We know 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 experience, we encourage you to apply.

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 research publications, present at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and help open-source key model artifacts and benchmarks   Who You Are:   You hold a PhD in Computer Science, Machine Learning, or a related field, with deep expertise in areas like generative modeling, world models, AI agents, reinforcement learning, or multimodal learning (or have equivalent experience) You have extensive experience designing and implementing deep learning models and agents, with a strong background in distributed training frameworks (e.g., DeepSpeed, Megatron-LM) and ML libraries (PyTorch) You have a track record of impactful publications at top-tier venues (e.g., NeurIPS, ICLR, ICML, TMLR) You are familiar with efficient training, post-training, and inference techniques for large foundation models You can explain complex models and research findings to both technical and non-technical audiences   Bonus Points (any of the following):   Experience bridging research and real-world product applications, especially with large foundation models, world models, or RL-trained agents Passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment Experience writing production data pipelines and applications Hands-on experience with GPU programming and optimization, including CUDA   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.  

As an Applied Science Intern, you will join a cross-functional team of engineers and applied scientists building AI-powered features within the Datadog platform. You will contribute to both existing and new products by improving machine learning models, developing new AI capabilities, and helping bring research into production. Working closely with engineering and product partners, you'll gain hands-on experience deploying AI systems at scale while strengthening your product mindset, collaboration, and communication skills. You'll also have opportunities to contribute to cutting-edge research that directly powers customer-facing features. 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: Design solutions for AI use cases by researching and benchmarking state-of-the-art models to identify the best approach Build highly scalable product features using generative AI, machine learning algorithms, and statistical techniques Contribute to the development, deployment, monitoring, and maintenance of production AI models, services, and infrastructure Explore, analyze, and derive insights from the large-scale observability data flowing through Datadog systems Participate in technical knowledge sharing by presenting recent academic research and investigating emerging model architectures to improve existing and future products Collaborate with engineering and product teams to productionize AI solutions and deliver customer-facing features   Who You Are: You will graduate in 2027 with a degree in Machine Learning, Artificial Intelligence, Statistics, Computer Science, or a related field You have experience building machine learning models through coursework, research, internships, or personal projects You have strong programming skills and are eager to learn new languages and technologies You communicate complex technical concepts clearly to both technical and non-technical audiences You have a strong interest in artificial intelligence, machine learning, and data-driven product development Available to complete a 6-month internship beginning in February, March, April, May, or June 2027 (we are unable to accommodate internships starting at any other time) Datadog values people from all walks of life. We know 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 experience, we encourage you to apply.  

Distributed Systems engineers at Datadog design, implement and run in production the foundational platforms powering our applications. Your data pipelines will ingest, store, analyze and query in real-time billions of events per second from companies all over the globe. The platforms are optimized for durability, high availability, low latency, internet-scale footprint and operability.  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: Build fault-tolerant, horizontally scalable solutions running in multi-tenant environments Write in Go, Java, Rust or C++, amongst other languages Use Kafka, Redis, Cassandra, Elasticsearch and other open-source components Own meaningful parts of our service, have an impact, grow with the company   Who You Are: You have a BS/MS/PhD in a scientific field or equivalent experience You have significant backend programming experience in one or more languages (Go, Java, Rust, C++)  You have been exposed to working on problems (high durability / low latency /…) You can get down to the low-level when needed You care about simple designs and performance You want to work in a fast, high-growth startup environment that respects its engineers and customers You’re excited about leveraging AI tools to enhance how you code, solve problems, and build – or eager to learn how You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output Bonus: You’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products 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.   

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 quality of post-training data when correctness, representativeness and difficulty pull in different directions? How do you evaluate an agent end to end when the trajectory is non-deterministic? Creating simulated agent environments for monitoring and SRE work is not well solved in open source or in published research, and Datadog is the leading company in this field. If those are the problems you want to spend your time on, come build this with us. What You'll Do: Own the applied science direction for GenSim: set the methodology and the forward-looking technical calls on how simulated environments and post-training data should be built, on a team where that decision-making does not exist yet. Define, measure and raise the quality of post-training data — basic correctness, representativeness against the real distribution of customer systems and production telemetry, and difficulty — and make those measures something the team can act on release over release. Close the realism gap. Simulated environments today are too clean and the injected problems are not yet hard enough; you'll drive the research and the engineering that make them look like real, imperfect production systems. Build scalable, production-grade systems rather than research scripts. The output is not just a dataset — it is a system of synthetic environments that must be reliable and invokable inside a training loop. Determine how this data is best applied, in LLM post-training and in evaluation, and own the agent and LLM application evaluation approaches for these environments. Work cross-functionally with the engineers and applied scientists on adjacent teams — Bits AI SRE, the model training effort, and the wider evaluation and experimentation pillar — so that what you learn moves freely in both directions. Who You Are: You have a PhD, MS or equivalent research experience in a scientific field, with strong applied mathematics grounding. 6+ years of relevant applied science or ML engineering experience, including setting technical direction for others. You have hands-on experience with LLM and agent post-training data: how it is created, managed, and how training-data quality is controlled. This is the requirement that matters most. You have real domain expertise in LLMs and agentic applications — not classical ML fine-tuning. Fine-tuning classifiers or traditional models is a different problem from the one this team is solving. You have evaluated agents or LLM applications, and can define what 'good' means before you measure it. You are a strong programmer and production software engineer. Python at minimum, plus the ability to ship scalable production systems and work with distributed systems. You collaborate well across engineering and science teams, and you're comfortable being the domain expert who decides what comes next. You thrive in ambiguity and can make sound technical calls when the path isn't yet defined. Bonus Points: Hands-on LLM fine-tuning, post-training or model training experience. Background in statistics, experiment design and data analysis. Experience deploying production-level ML infrastructure. Observability or monitoring systems background. Architecture-level understanding of LLMs.

Datadog’s Product Analytics suite spans Product Analytics, Session Replay, Feature Flags, and Experimentation. Together they give product teams a complete, quantitative and qualitative picture of how users experience their applications, plus the tools to release, measure, and improve those experiences with confidence. Our team of Applied Scientists makes this space smarter and more autonomous, researching, prototyping, and industrializing AI/ML capabilities that make the suite proactive and agentic by default: AI-driven event understanding and instrumentation, conversational analytics, automated insight reporting, and UI/UX issue detection from session replays. The ambition is to let product teams act with autonomy, moving from question to insight to safely released change without depending on engineering or analysts. As the Engineering Manager for this team, you’ll lead a team of Applied Scientists through an early-stage, high-impact opportunity: defining the team’s technical vision, growing its footprint, and shaping how AI capabilities get built and delivered across the suite. This is greenfield work, both in the R&D itself and in the team you’ll grow around it. 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: Lead and grow a team of Applied Scientists researching, prototyping, and industrializing AI/ML capabilities across the suite Define the technical vision and roadmap for the suite’s agentic and proactive AI/ML capabilities: event understanding, conversational analytics, insight reporting, and session-replay issue detection Stay hands-on as a technical contributor and reviewer, helping the team move from research prototypes to production-grade capabilities Shape how AI capabilities get built and delivered across the suite, partnering closely with product and engineering leadership Hire and onboard Applied Scientists as Datadog scales its investment in applied AI/ML Represent the team’s progress and priorities to stakeholders across Product Analytics and Datadog’s broader AI efforts Who You Are: An experienced technical leader with a background in applied AI/ML, ideally with hands-on research or applied science experience of your own A people manager, whether established or early in your management journey, backed by strong technical credibility Comfortable operating in greenfield ambiguity: defining direction and structure where little exists yet, rather than executing against an established roadmap Experience taking ML/AI capabilities from research prototype to production, ideally in an analytics, NLP, or user-behavior domain A clear communicator who can translate technical AI/ML concepts for product and engineering stakeholders Motivated by early-stage, high-impact work and comfortable with the ambiguity that comes with it 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.

As a Staff Engineer on the Data Platform Experience team, you'll help shape how Datadog engineering teams build, operate, and evolve products on the Observability Data Platform. You'll lead the design and delivery of shared platform capabilities that reduce developer friction, improve operational visibility, and enable engineering teams to move faster with confidence. This role combines deep distributed systems expertise with technical leadership across multiple teams, influencing platform strategy while remaining hands-on in the code. You'll have the opportunity to solve company-wide challenges spanning cost intelligence, operational tooling, platform health, and developer experience. 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: Lead strategic engineering initiatives that improve how product teams build, operate, and evolve services on the Observability Data Platform. Design and build scalable platform capabilities for cost intelligence, including cloud cost allocation, trend analysis, and optimization recommendations. Develop operational intelligence and self-service tooling that helps engineering teams understand platform health, troubleshoot incidents, and improve operational efficiency. Drive reusable platform services and developer workflows that increase engineering autonomy while reducing operational complexity across multiple products. Provide technical leadership across teams by influencing architecture, mentoring engineers, and raising engineering standards through hands-on technical contributions. Participate in the team's on-call rotation and continuously improve platform reliability, observability, and operational excellence. Who You Are: You have experience designing and building large-scale SaaS or cloud platforms with deep expertise in distributed systems. You have strong software architecture and system design skills and have successfully led complex technical initiatives across multiple teams. You have a platform engineering mindset and enjoy building reusable capabilities that enable other engineering organizations to move faster. You are an effective communicator who can influence technical direction without direct authority and collaborate across organizational boundaries. You have experience with backend technologies such as Java and Go, and distributed infrastructure including Kafka and Kubernetes Experience with observability platforms, internal developer platforms, operational tooling, developer productivity solutions, or AI-powered engineering workflows is beneficial but not required. Datadog values people from all walks of life. We know 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 experience, we encourage you to apply.

We’re looking for an Engineering Manager to lead our Sensitive Data Scanner (SDS) Telemetry team. The SDS group’s mission is to be the world’s easiest-to-use tool to discover, classify, manage, and report sensitive data risks across cloud, on-premise, and code environments. This team builds and scales the detection capabilities that scan all telemetry data flowing into Datadog — logs, APM spans, and RUM events — operating in streaming, at processing time, and at very large scale. You’ll lead a small, close-knit team based in Paris, with the opportunity to shape how the team grows as SDS Telemetry’s scope expands. It’s a chance to combine hands-on technical leadership with direct customer and product impact in the security and observability 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 Datadogs can create a work-life harmony that best fits them.   What You’ll Do: Lead and grow a team of engineers building real-time sensitive data detection across Datadog’s Logs, APM, and RUM telemetry pipelines Partner closely with the Logs, APM, and RUM teams, plus Datadog’s Trust & Safety team, to align on roadmap and integration priorities Shape product direction by working closely with Product, grounding decisions in customer needs and business impact Stay hands-on: contribute to design decisions and participate in the team’s on-call rotation Recruit, mentor, and develop engineers as the team grows beyond its initial size Help build a strong engineering culture as part of Datadog’s broader Sensitive Data Scanner group   Who You Are: You have experience building and shipping revenue-generating products, with strong product acumen and a customer-first mindset You have hands-on experience with Go and/or Java, and a track record building distributed, streaming systems at scale You have experience managing engineers — or are ready to step into your first formal management role — and know how to set goals, give feedback, and help a team grow You measure success through customer and business outcomes, not just output shipped You’re comfortable weighing tradeoffs between speed and correctness, ideally from experience in a fast-moving environment   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.  

We are looking for a strong technical leader to join the Private Action Runner team, part of the larger Action Platform group  and help shape one of the core execution layers behind Datadog’s action-taking and remediation capabilities. Private Action Runner (PAR) enables Datadog products and AI agents to securely run actions inside customer infrastructure with controls for authentication, permissions, auditing and safe execution.  The role will be hands-on, covering architecture, implementation, reliability and collaboration with teams integrating PAR across Datadog. It also offers leadership exposure through leading a team of 3 engineers, with the expectation that the role will quickly transition into a formal Engineering Manager 1 position as the team grows. 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: (Describe role responsibilities here) Solve a scaling bottleneck in a critical service Deploy a new feature to production, progressively rolling it out with feature flags Investigate and fix a production issue from a service your team owns Design a way to scale up a service for more traffic With your team, plan the most important projects to work on next Mentor and lead a small team of 3 engineers    Who You Are: (Describe role qualifications here) You have significant experience in one or more languages You value code simplicity and performance You can design architecture to solve problems at high scale You have a BS/MS/PhD in a scientific field or equivalent experience You want to work in a fast, high-growth startup environment that respects its engineers and customers You’re excited about leveraging AI tools to enhance how you code, solve problems, and build – or eager to learn how You have demonstrated ability to use AI coding tools in day-to-day workflows and build, validate, and refine AI-generated output in products You can design AI Backend systems, with awareness of quality, cost, and latency tradeoffs You are willing to lead and support a small group of engineers Bonus: You have previous experience mentoring or leading small teams as a tech lead or team lead  Bonus: You’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products 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.