Staff Research Scientist - Post-Training for Agents

As a Staff Research Scientist within Datadog AI Research (DAIR), you will 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 the researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products.

What You'll Do:

• 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

Who You Are:

• You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as reinforcement learning, AI agents, post-training, or generative modeling
• You have driven technically ambitious research at meaningful scale as an individual contributor, whether in an industry research lab, startup, academic environment, or another research setting
• You have extensive hands-on experience designing and implementing reinforcement learning systems, agent training methods, simulated environments, synthetic data pipelines, or evaluation frameworks
• You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements
• You set technical direction through influence rather than authority, and you have mentored other researchers or engineers and elevated the quality of work around you
• You want to stay deeply hands-on in research for the long term, and you can communicate complex research findings effectively across technical and non-technical audiences

Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.

The reasonably estimated yearly salary for this role at Datadog is: $320,000 — $400,000 USD

Datadog is an Affirmative Action and Equal Opportunity Employer and 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, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.

Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice.