Drive post-training research for multimodal decision models at Datadog, a leader in observability and security.
Posted by employer 7 hours ago
First seen on Joblaze 5 hours ago
Last verified on the company career page 5 hours ago
Skills & Technologies
What you'll build
Must have
AI in the day-to-day
null
Requirements
Not disclosed in this posting: compensation, years of experience, visa sponsorship.
Joblaze summary
In the role of Staff Research Scientist at Datadog, the individual will focus on advancing post-training research for multimodal decision models, tackling complex challenges in observability and security. Key skills include expertise in AI agents, decision models, and synthetic data generation, with a strong emphasis on hands-on experience in research and model development. This position is suited for a senior-level candidate with a proven track record in impactful research and the ability to mentor others. The role also involves collaboration with cross-functional teams to translate research into practical applications.
Joblaze insights
Quick facts
From the original posting
As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive work on fundamental research in post-training and multimodal observability-native decision models as a hands-on individual contributor. You will advance post-training approaches, simulated environments, synthetic data generation, and evaluation methods for agents operating across complex real-world systems. You will help 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 post-training research for multimodal decision models and hybrid decision/generative systems to address ambitious research challenges in observability and security.
Own research problems end to end, from framing the question through experimentation, model development, and evaluation
Design and advance post-training methods and training loops for models operating in complex environments
Build simulated environments, synthetic data generation approaches, and evaluation frameworks that enable scalable model training and rigorous measurement
Collaborate with cross-functional teams across Research, Product, and Engineering to translate research advances into scalable Datadog capabilities
Share research findings with external audiences upon reaching significant project milestones.
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 AI agents, decision models, 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 or implementing 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 have operated at a senior technical level and have experience leading, 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 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.
#LI-Hybrid
About Datadog:
Standard company text repeated across Datadog's postings is omitted here.