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Salary: USD 320,000 - 400,000 / annual
Datadog AI Research (DAIR) is seeking a Staff Research Scientist to drive research in post-training and autonomous agents as a hands-on individual contributor. You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will set the technical direction for ambitious research programs, raise the technical bar for researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products.
Key responsibilities include:
- Drive research in agent post-training and reinforcement learning, shaping the technical direction of ambitious research programs grounded in observability and security
- Own research problems end to end, from framing the question through experimentation, model development, and evaluation
- Design and advance reinforcement learning approaches, post-training methods, and training loops for agents operating in complex environments
- Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement
- Raise the technical bar across the team by reviewing research directions, mentoring researchers and research engineers, and setting standards for experimental rigor
- Collaborate with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities
- Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks
Datadog is a global SaaS business delivering observability and security solutions. The company is used by organizations of all sizes across a wide range of industries and is trusted by Fortune 500 companies and high-growth AI leaders.
Requirements:
- PhD in Computer Science, Machine Learning, or a related field, or equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling
- Driven technically ambitious research at meaningful scale as an individual contributor, whether in an industry research lab, startup, academic environment, or another research setting
- Extensive hands-on experience designing and implementing reinforcement learning systems, agent training methods, simulated environments, synthetic data pipelines, or evaluation frameworks
- Track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements
- Ability to set technical direction through influence rather than authority, with experience mentoring other researchers or engineers and elevating the quality of work around you
- Commitment to staying deeply hands-on in research for the long term, with ability to communicate complex research findings effectively across technical and non-technical audiences
