Senior AI Engineer - APM Experiences

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.

• 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.

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

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

• 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.