AI Research Engineer - Datadog AI Research (DAIR)
Datadog - New York, New York, USA
Posted Aug 21, 2025
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
- Not verified last checked Jun 13, 2026
- Salary
- Not verified not verified - source not recorded; timestamp not recorded
- 401(k) match
- Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
AI Research Engineer - Datadog AI Research (DAIR) New York, New York, USA As a Research Engineer on our team, you will partner with Research Scientists to turn research ideas into working systems, building the data, tooling, and infrastructure that enable rapid iteration, trustworthy evaluation, and a smooth path from prototype to production. Building on our track record of AI-powered solutions (e.g., Bits AI , Bits Evolve , and our time series foundation model ), Datadog AI Research tackles high-risk, high-reward problems grounded in real-world challenges in cloud observability and security. We are focused on two research areas: World Models for Observability -- Training multimodal foundation models that learn the joint dynamics of distributed systems across metrics, traces, logs, topology, and events. These models power advanced forecasting, anomaly detection, root cause analysis, counterfactual simulation ("what if?"), and provide a learned planning backbone for our autonomous agents. Trained Agents for Observability -- Post-training models to operate autonomously across Datadog's domain. SRE incident response is our first target, with a clear path to code repair, security response, and infrastructure optimization. We build the simulation environments, RL training loops, and evaluation infrastructure needed to train agents that match or surpass frontier models at a fraction of the cost. What You'll Do: Build and operate multimodal data pipelines, training and evaluation infrastructure, benchmarks, and internal tooling Implement models, run experiments at scale, and profile for reliability, performance, and cost Build simulation environments and replay infrastructure for agent training and evaluation Orchestrate distributed training and
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Apply link verified; last checked Jun 13, 2026.
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