About this role
Guiding the technical direction of ambitious research programs, this position involves training large-scale multimodal models on diverse telemetry data such as metrics, logs, traces, topology, and events. Practitioners balance hands-on experimentation, model development, and technical problem-solving with the responsibility of leading and mentoring a team of researchers and research engineers.
Operating within Datadog AI Research (DAIR) in Paris, the team focuses on fundamental research in foundation models, world models, and multimodal learning. Collaborating closely with product and engineering departments, the group turns research advances into scalable platform capabilities while contributing to publications and open-source artifacts.
Ideal candidates maintain a strong hands-on research identity while developing high-performing teams. Applicants should possess a PhD or equivalent experience in a relevant field, coupled with a track record of driving large-scale deep learning initiatives and sharing findings at top-tier venues.
What you will do
- Lead research in foundation models, world models, and multimodal learning while setting technical direction for observability and security programs.
- Guide and mentor researchers and research engineers while staying closely involved in strategy, experimentation, and model development.
- Train large-scale multimodal models on telemetry data including metrics, logs, traces, topology, and events.
- Advance approaches to pre-training, representation learning, world modeling, scaling, and evaluation for complex distributed systems.
- Collaborate with cross-functional teams across Research, Product, and Engineering to translate research advances into scalable capabilities.
- Contribute to research publications, present at top-tier conferences like NeurIPS, ICLR, and ICML, and help open-source key artifacts.
What you need
- Hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience.
- Demonstrate deep expertise in foundation models, world models, multimodal learning, or generative modeling.
- Show a proven ability to lead technically ambitious research at meaningful scale in industry, academia, or other settings.
- Bring extensive hands-on experience designing, training, or evaluating large-scale deep learning models.
- Operate at a senior technical level with prior experience leading, mentoring, and developing teams.
- Communicate complex research findings effectively across technical and non-technical audiences.
Nice to have
- Experience in multimodal or non-text data
- Influential research publications
- Significant model or system contributions and widely used research artifacts
Benefits
- High income earning opportunities based on individual performance
- New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
- Continuous professional development, product training, and career pathing
- Intradepartmental mentor and buddy program for in-house networking
- Inclusive company culture with employee resource groups and inclusion talks
- Free, global Spring Health benefits for employees and dependents age 6+
Skills and tech stack
Questions candidates ask
What is the work policy for this role?
Datadog operates as a hybrid workplace to ensure employees can create a work-life harmony that best fits them, while placing value in office culture, relationships, and collaboration.
What kind of data will the team work with?
The team trains large-scale multimodal models on diverse telemetry data, including metrics, logs, traces, topology, events, and other non-text modalities.
What are the expectations regarding publications and conferences?
Team members contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks.