Datadog
Staff Applied Scientist - Observability Data Platform
Datadog, Denver, Colorado, United States
Overview
Staff Applied Scientist - Observability Data Platform role at Datadog. The Observability Data Platform (ODP) powers the core of Datadog’s telemetry systems, handling exabytes of multimodal observability data. As AI agents become first-class consumers of telemetry, ODP is evolving to meet their demands — scaling with data growth, exposing new query mechanisms, rethinking storage, transformation, and serving, and enforcing guardrails for security and reliability. Our team is building an intelligent control plane for production systems, moving beyond passive monitoring to enable AI agents to safely and effectively act in live environments. We integrate techniques from symbolic reasoning, formal methods, and generative AI. We are looking for an experienced Staff Applied Scientist with a background spanning systems engineering, AI, and formal reasoning. Expertise in causal modeling, generative simulation, runtime verification, reinforcement learning, or related areas is valued, with a focus on building reliable systems. You will join the team behind Datadog’s most ambitious projects: evolving observability infrastructure for stochastic, self-improving systems. Datadog values collaboration and a healthy work-life balance in a hybrid workplace. What You’ll Do
Design and prototype intelligent systems for AI-native observability, including cost-aware agent orchestration, adaptive query execution, and self-optimizing components. Lead efforts to apply reinforcement learning, search, or hybrid approaches to infrastructure-level decision-making (autoscaling, scheduling, load shaping). Collaborate with AI researchers and platform engineers to design experimentation loops and verifiers that guide LLM outputs using runtime metrics and formal models. Explore AI paradigms such as AI compilers, programming after code, and runtime-aware prompt engineering to inform Datadog’s infrastructure and product design. Help define the direction of BitsEvolve, Datadog’s optimization agent using LLMs and evolutionary search to discover code improvements, optimize GPU kernels, and tune configurations for performance. Partner with product teams and platform stakeholders to translate scientific advances into measurable improvements in cost, performance, and observability depth. Who You Are
BS/MS/PhD in a scientific field or equivalent experience 8+ years of experience in systems engineering, database internals, or infrastructure research, including hands-on production experience Strong software engineering foundation in C++, Rust, Go, or Python; ability to write performant, maintainable code Deep expertise in at least one of: query optimization, data center scheduling, compiler design, reinforcement learning, or distributed systems design Experience applying search, planning, or learning techniques to real-world optimization problems Interest in systems that learn, adapt, and improve using runtime metrics and human-defined objectives Hypothesis-driven with experience designing experiments and evaluation loops (simulations, benchmarks, or live systems) Thrives in ambiguity, enjoys reading papers and building prototypes, and wants to shape infrastructure for the AI era Collaborates across research, engineering, and product to translate scientific insights into practical outcomes Benefits and Growth
Build tools for software engineers and leverage the tools you build to accelerate development Influence product direction and impact on the business Work with skilled, knowledgeable, and collaborative teammates Competitive global benefits Continuous professional development About Datadog
Datadog (NASDAQ: DDOG) is a global SaaS company delivering growth and profitability by enabling digital transformation, cloud migration, and infrastructure monitoring across customer technology stacks. We value professional development, diversity of thought, innovation, and work excellence. Learn more about Datadog Life on Instagram, LinkedIn, and the Datadog Learning Center. Equal Opportunity
Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other protected characteristics. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See our Candidate Legal Notices for reference. Datadog provides accessibility assistance on request for applicants and can accommodate completion of the application process. Privacy and AI guidelines apply to all applicant information submitted.
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Staff Applied Scientist - Observability Data Platform role at Datadog. The Observability Data Platform (ODP) powers the core of Datadog’s telemetry systems, handling exabytes of multimodal observability data. As AI agents become first-class consumers of telemetry, ODP is evolving to meet their demands — scaling with data growth, exposing new query mechanisms, rethinking storage, transformation, and serving, and enforcing guardrails for security and reliability. Our team is building an intelligent control plane for production systems, moving beyond passive monitoring to enable AI agents to safely and effectively act in live environments. We integrate techniques from symbolic reasoning, formal methods, and generative AI. We are looking for an experienced Staff Applied Scientist with a background spanning systems engineering, AI, and formal reasoning. Expertise in causal modeling, generative simulation, runtime verification, reinforcement learning, or related areas is valued, with a focus on building reliable systems. You will join the team behind Datadog’s most ambitious projects: evolving observability infrastructure for stochastic, self-improving systems. Datadog values collaboration and a healthy work-life balance in a hybrid workplace. What You’ll Do
Design and prototype intelligent systems for AI-native observability, including cost-aware agent orchestration, adaptive query execution, and self-optimizing components. Lead efforts to apply reinforcement learning, search, or hybrid approaches to infrastructure-level decision-making (autoscaling, scheduling, load shaping). Collaborate with AI researchers and platform engineers to design experimentation loops and verifiers that guide LLM outputs using runtime metrics and formal models. Explore AI paradigms such as AI compilers, programming after code, and runtime-aware prompt engineering to inform Datadog’s infrastructure and product design. Help define the direction of BitsEvolve, Datadog’s optimization agent using LLMs and evolutionary search to discover code improvements, optimize GPU kernels, and tune configurations for performance. Partner with product teams and platform stakeholders to translate scientific advances into measurable improvements in cost, performance, and observability depth. Who You Are
BS/MS/PhD in a scientific field or equivalent experience 8+ years of experience in systems engineering, database internals, or infrastructure research, including hands-on production experience Strong software engineering foundation in C++, Rust, Go, or Python; ability to write performant, maintainable code Deep expertise in at least one of: query optimization, data center scheduling, compiler design, reinforcement learning, or distributed systems design Experience applying search, planning, or learning techniques to real-world optimization problems Interest in systems that learn, adapt, and improve using runtime metrics and human-defined objectives Hypothesis-driven with experience designing experiments and evaluation loops (simulations, benchmarks, or live systems) Thrives in ambiguity, enjoys reading papers and building prototypes, and wants to shape infrastructure for the AI era Collaborates across research, engineering, and product to translate scientific insights into practical outcomes Benefits and Growth
Build tools for software engineers and leverage the tools you build to accelerate development Influence product direction and impact on the business Work with skilled, knowledgeable, and collaborative teammates Competitive global benefits Continuous professional development About Datadog
Datadog (NASDAQ: DDOG) is a global SaaS company delivering growth and profitability by enabling digital transformation, cloud migration, and infrastructure monitoring across customer technology stacks. We value professional development, diversity of thought, innovation, and work excellence. Learn more about Datadog Life on Instagram, LinkedIn, and the Datadog Learning Center. Equal Opportunity
Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other protected characteristics. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See our Candidate Legal Notices for reference. Datadog provides accessibility assistance on request for applicants and can accommodate completion of the application process. Privacy and AI guidelines apply to all applicant information submitted.
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