Senior AI Engineer - APM Experiences

The opportunity

Datadog’s APM Experiences team owns the core product experience for Application Performance Monitoring — including distributed tracing, service representation, and more. We’re building a new wave of AI-powered capabilities that help customers detect, resolve, and prevent performance issues faster. In this role, you will lead end‑to‑end development of LLM- and Agent‑based features that can:

• Debug and investigate application performance issues down to the root cause, as both a developer assistant and a fully autonomous agent
• Proactively recommend performance and reliability-based optimizations to prevent the next incident
• Automatically create intelligent monitors and SLOs for the most important business flows and critical paths

This is a highly product‑minded engineering role: you’ll work from problem discovery and UX all the way to reliable, scalable production systems.

What you’ll do

• Shape AI experiences for APM. Design and ship LLM/agentic workflows that analyze traces, metrics, logs, and other telemetry to generate diagnoses, explanations, and guided fixes.
• Own the full loop. Prototype quickly, define success metrics and evals, run experiments, iterate, and ultimately productionize for scale and reliability.
• Build robust agent systems. Develop tools, retrieval and planning strategies, and guardrails; manage prompts/evals; design fallbacks and human‑in‑the‑loop paths.
• Integrate with Datadog’s platform. Leverage surfaces like Trace Explorer, Service Catalog, monitors, and workflows to deliver end‑to‑end value in the APM UI.
• Partner deeply. Collaborate with PM, Design, and partner teams to build cohesive experiences.
• Raise the bar on engineering. Write performant, maintainable backend code, own services in production, and improve reliability for high‑throughput, low‑latency data systems.

Who you are

Product‑minded engineer who ships AI to production

• 4+ years building backend or real-time ML systems; you value simplicity, correctness, and performance
• Proven experience delivering LLM/agent features to production (prompting, tooling, evals, safety/guardrails)
• Comfortable owning user journeys, iterating from prototype → alpha → GA, and measuring impact with clear product metrics
• You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output
• You’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products

Strong ML / applied science fundamentals

• Solid grasp of the ML lifecycle (task definition, dataset collection, modeling, evaluation, deployment, iteration) and statistics (experiment design, confidence intervals)
• Experience choosing/modeling the right technique for the job (e.g., anomaly detection, ranking/recommendation, NLP), and knowing when a heuristic beats a model
• Fluency with offline/online evals for AI systems; can build reliable golden sets and automatic regressions

Distributed systems & observability savvy

• Experience with microservices performance: tracing, latency breakdowns, concurrency, and resiliency patterns
• Proficient in Go, Java, or Python; strong API/service design; production ops (monitoring, alerting, on‑call rotation)

Nice to have

• Hands‑on with distributed tracing stacks (OpenTelemetry/Datadog APM), profilers, and logs/metrics pipelines
• Exposure to planning/agent frameworks, tool‑use orchestration, RAG, and retrieval/indexing for observability data
• Familiarity with SLO/SLA practices and incident response

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.

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

#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:

{#salary}$187,000—$240,000 USD{/salary}

About Datadog:

Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.

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.

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