Staff Software Engineer - ML Observability
The ML Observability team builds cutting-edge tools to monitor, explain, and improve AI systems in production, particularly those leveraging Large Language Models (LLMs) and generative AI. We provide robust, scalable observability for AI workloads, including drift detection and model evaluation, and behavior tracing, e
What this role actually needs.
The ML Observability team builds cutting-edge tools to monitor, explain, and improve AI systems in production, particularly those leveraging Large Language Models (LLMs) and generative AI. We provide robust, scalable observability for AI workloads, including drift detection and model evaluation, and behavior tracing, e Responsibilities: - Drive design and implementation of LLM observability features. - Ideate, prototype, and scale new product features to provide insights and drive improvements for generative AI systems - Work cross-functionally with other eng teams, product, UX, and applied science to iterate fast and find product-market fit - Develop and extend tools for tracing, evaluating, and debugging LLMs - Influence architecture decisions and mentor engineers to build resilient, high-performance systems - Stay close to customer pain points and use those insights to guide product and engineering priorities Requirements: - Get to build tools for software engineers, just like yourself. And use the tools we build to accelerate our development. - Have a lot of influence on product direction and impact on the business . - Work with skilled, knowledgeable, and kind teammates who are happy to teach and learn - Competitive global benefits - Continuous professional development Benefits: - Get to build tools for software engineers, just like yourself. And use the tools we build to accelerate our development. - Have a lot of influence on product direction and impact on the business . - Work with skilled, knowledgeable, and kind teammates who are happy to teach and learn - Competitive global benefits - Continuous professional development Company context: Datadog operates a cloud observability and security platform with meaningful hiring across infrastructure, security, AI, and developer tooling.
Day-to-day expectations
Datadog lists these responsibilities for the Staff Software Engineer - ML Observability role.
- Drive design and implementation of LLM observability features.
- Ideate, prototype, and scale new product features to provide insights and drive improvements for generative AI systems
- Work cross-functionally with other eng teams, product, UX, and applied science to iterate fast and find product-market fit
- Develop and extend tools for tracing, evaluating, and debugging LLMs
- Influence architecture decisions and mentor engineers to build resilient, high-performance systems
- Stay close to customer pain points and use those insights to guide product and engineering priorities
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- Get to build tools for software engineers, just like yourself. And use the tools we build to accelerate our development.
- Have a lot of influence on product direction and impact on the business .
- Work with skilled, knowledgeable, and kind teammates who are happy to teach and learn
- Competitive global benefits
- Continuous professional development
Why people would want this job
Datadog published these compensation, benefits, or working-context details with the role.
- Get to build tools for software engineers, just like yourself. And use the tools we build to accelerate our development.
- Have a lot of influence on product direction and impact on the business .
- Work with skilled, knowledgeable, and kind teammates who are happy to teach and learn
- Competitive global benefits
- Continuous professional development
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Staff Software Engineer - ML Observability is framed against UpJobz source checks, country scope, compensation visibility, and work-authorization signals so candidates can make a faster go/no-go decision.
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Source: careers.datadoghq.com · Source ID: 7107437 · Confidence: 93/100 · Last checked: May 7, 2026
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