Staff AI Engineer - Agent Observability

The Agent 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, enabling customers to ship AI with confidence.

As a Staff Engineer, you’ll lead the development of new features and foundational capabilities within Datadog’s LLM Observability product. You will shape product direction, drive experimentation, and apply your deep understanding of both AI systems and software engineering to solve open-ended problems in the fast-moving AI landscape. Your work will directly impact how our customers monitor, troubleshoot, and optimize LLM-based applications in production.

Join us in building the foundational tools that make AI systems observable, understandable, and reliable in the real world.

At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• 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
• Stay current with industry trends and advancements in machine learning and observability, driving innovation within the team

Who You Are:

• You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent experience
• Deep understanding of distributed systems and scalable backend architectures
• Hands-on experience building and shipping LLM-powered or GenAI applications.
• Understanding of model internals, inference pipelines, evaluation techniques, and prompt engineering
• Ability to thrive in ambiguous, fast-changing spaces and have a product-oriented mindset
• You’re excited to shape the next generation of AI observability tools from the ground up
• Communicate clearly, think rigorously, and take pride in clean, maintainable code
• Experience with observability tools/platforms

Benefits and Growth:

• 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

#LI-Hybrid

Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.

The reasonably estimated yearly salary for this role at Datadog is:

$234,000—$300,000 USD

Equal Opportunity at Datadog:

Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.