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

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