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Salary: USD 244,000 - 305,000 / annual
Datadog AI Research (DAIR) is seeking a Research Manager to lead a team working on fundamental research in post-training and autonomous agents. This role combines technical leadership with hands-on research involvement, ideal for an exceptional researcher or technical leader who wants to maintain a strong research identity while developing and leading a high-performing team.
You will advance reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. Your responsibilities include:
- Leading research in agent post-training and reinforcement learning, setting technical direction for ambitious research programs grounded in observability and security
- Guiding and mentoring researchers and research engineers while remaining closely involved in research strategy, experimentation, model development, and technical problem-solving
- Designing and advancing reinforcement learning approaches, post-training methods, and training loops for agents in complex environments
- Building simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable agent training and rigorous measurement
- Collaborating with cross-functional teams across Research, Product, and Engineering to translate advances in autonomous agents into scalable Datadog capabilities
- Contributing to research publications, presenting at top-tier conferences (NeurIPS, ICLR, ICML), and helping open-source key model artifacts and benchmarks
Datadog operates as a hybrid workplace with offices in New York City and Paris. The role offers competitive global benefits, new hire stock equity (RSUs), employee stock purchase plan (ESPP), conference attendance opportunities, mentorship programs, and an inclusive company culture.
REQUIREMENTS:
- PhD in Computer Science, Machine Learning, or related field, or equivalent experience
- Deep expertise in reinforcement learning, AI agents, post-training, or generative modeling
- Demonstrated ability to lead technically ambitious research at meaningful scale (industry research lab, startup, academic environment, or similar)
- Extensive hands-on experience designing or 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
- Senior technical level experience with demonstrated leadership, mentoring, and team development of researchers or engineers while remaining deeply hands-on in technical direction and execution
- Ability to communicate complex research findings effectively across technical and non-technical audiences
