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Observability Datadog Jobs (NOW HIRING)

Neuberger Berman

Observability Platform Engineer

New York, NY · On-site

$62.25 - $82.75/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Serve as the primary Datadog platform owner - architecting, building, and maintaining scalable observability solutions across cloud and on-prem environments (Windows and Linux/Unix), with direct ...

Neuberger Berman

Observability Platform Engineer

Manhattan, NY · On-site

$62.75 - $83.50/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Serve as the primary Datadog platform owner - architecting, building, and maintaining scalable observability solutions across cloud and on-prem environments (Windows and Linux/Unix), with direct ...

Neuberger Berman Group LLC

Observability Platform Engineer

New York, NY · On-site

$62.25 - $82.75/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Serve as the primary Datadog platform owner - architecting, building, and maintaining scalable observability solutions across cloud and on-prem environments (Windows and Linux/Unix), with direct ...

CYNET SYSTEMS

Observability Architect

Portsmouth, NH · On-site

$71/hr

Design and implement scalable Datadog monitoring solutions across infrastructure, applications ... Drive observability adoption across hybrid cloud and enterprise platforms. * Integrate ...

MX Technologies, Inc.

Sr. Observability Engineer

Lehi, UT · On-site

$98K - $134K/yr

You shepherd Datadog and partner with our product engineering teams so they observe the right ... Observability and incident response run one on-call roster. You take shifts with the rest of the ...

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Observability Datadog information

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How much do observability datadog jobs pay per hour?

As of Aug 14, 2026, the average hourly pay for observability datadog in the United States is $17.34, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $18.03 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an observability Datadog specialist?

To excel as an Observability Engineer with a focus on Datadog, you need a strong background in IT operations, cloud infrastructure, and monitoring concepts, often supported by relevant degrees or certifications. Familiarity with Datadog's platform, scripting languages (like Python or Bash), and integrations with cloud services (AWS, Azure, GCP) is typically required. Analytical thinking, proactive problem-solving, and the ability to collaborate across teams are vital soft skills in this role. These skills ensure effective system monitoring, rapid incident response, and ongoing performance optimization in complex environments.

What is an observability Datadog specialist?

Observability Datadog roles typically refer to professionals who implement, manage, and optimize observability practices using the Datadog platform. These specialists focus on monitoring application performance, infrastructure health, and ensuring real-time visibility into system operations. They configure dashboards, set alerts, and analyze logs, traces, and metrics to detect and resolve issues quickly. Their work helps organizations maintain system reliability, optimize performance, and improve incident response.

How does an observability Datadog specialist typically collaborate with development and operations teams?

An Observability Datadog specialist works closely with both development and operations teams to ensure that applications and infrastructure are properly monitored. They often participate in sprint planning and incident response meetings, helping teams define meaningful metrics, set up dashboards, and configure alerting policies. Collaboration also includes training team members on best practices for using Datadog and troubleshooting monitoring issues together. This cross-functional role ensures that all stakeholders have visibility into system health and can respond quickly to performance or reliability concerns.

What is the difference between Observability Datadog vs Cloud Engineer?

AspectObservability DatadogCloud Engineer
Primary FocusMonitoring, analytics, and visualization of system performanceDesigning, implementing, and managing cloud infrastructure
Required SkillsMonitoring tools, scripting, data analysisCloud platforms, scripting, infrastructure as code
CertificationsDatadog certifications, cloud provider certificationsAWS, Azure, or GCP certifications
Work EnvironmentIT operations, DevOps teamsCloud infrastructure teams, DevOps

While both roles involve cloud technologies, Observability Datadog specialists focus on monitoring and analyzing system performance, whereas Cloud Engineers design and maintain cloud infrastructure. Understanding these differences helps organizations assign the right skills to each role.

Infographic showing various Observability Datadog job openings in the United States as of August 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 74% Physical, 8% Hybrid, and 18% Remote job distribution, with an average salary of $36,065 per year, or $17.3 per hour.

AI Research Scientist - Datadog AI Research (DAIR)

Datadog

Manhattan, NY • On-site

Full-time

Re-posted 26 days ago

Datadog rating

9.6

Company rating: 9.6 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

1st of 491 rated business services

Job description

Job Summary:
Datadog is a global SaaS business focused on breaking down silos in cloud technology. The AI Research Scientist will engage in fundamental research and collaborate with engineering teams to develop AI-powered solutions for cloud observability and security.
Responsibilities:
• Conduct research in generative AI and machine learning, building specialized foundation models and trained agents for observability
• Train multimodal models on large-scale, diverse telemetry data (metrics, logs, traces, topology, events) using distributed training infrastructure
• Design and build simulated environments and RL training loops for on-policy agent training and evaluation
• Collaborate with cross-functional teams (Product, Engineering) to integrate capabilities like multimodal world modeling and autonomous agents into Datadog's products
• Stay at the forefront of foundation models, world models, and RL-based agent research
• Contribute to research publications, present at top-tier conferences (e.g., NeurIPS, ICLR, ICML), and help open-source key model artifacts and benchmarks
Qualifications:
Required:
• You hold a PhD in Computer Science, Machine Learning, or a related field, with deep expertise in areas like generative modeling, world models, AI agents, reinforcement learning, or multimodal learning (or have equivalent experience)
• You have extensive experience designing and implementing deep learning models and agents, with a strong background in distributed training frameworks (e.g., DeepSpeed, Megatron-LM) and ML libraries (PyTorch)
• You have a track record of impactful publications at top-tier venues (e.g., NeurIPS, ICLR, ICML, TMLR)
• You are familiar with efficient training, post-training, and inference techniques for large foundation models
• You can explain complex models and research findings to both technical and non-technical audiences
Preferred:
• Experience bridging research and real-world product applications, especially with large foundation models, world models, or RL-trained agents
• Passion for pushing the boundaries of AI with a focus on customer impact and scalable deployment
• Experience writing production data pipelines and applications
• Hands-on experience with GPU programming and optimization, including CUDA
Company:
Datadog is an observability and security platform that offers infrastructure, applications, software development, and monitoring services. Founded in 2010, the company is headquartered in New York, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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