Requirements

6+ years building backend systems (Go, Java, or .NET) with strong focus on simplicity, correctness, and performance.
Proven experience delivering LLM/agent features to production (prompting, tooling, evals, safety/guardrails).
Comfortable navigating ambiguity, iterating from prototype to production, and measuring impact with clear metrics.
Solid grasp of the ML lifecycle (task definition, dataset construction, modeling, evaluation, deployment, iteration) and statistics for experiments.
Fluency with offline/online evals: golden sets, automated regressions, and evaluation harnesses that prevent silent quality drift.
Experience with microservices performance: tracing, latency breakdowns, concurrency, resiliency patterns.
Production operations mindset: monitoring, alerting, and participating in on-call rotations where applicable.

Nice to Haves

Hands-on with distributed tracing stacks (OpenTelemetry/Datadog APM), profilers, and logs/metrics pipelines.
Experience with planning/agent frameworks, tool-use orchestration, RAG, and retrieval/indexing over large context.
Experience building developer tools (IDEs, static analysis, compilers, code transformation pipelines) or code-generation systems.
Familiarity with semantic conventions/schema migrations, scenario-based testing, and cross-language porting at scale.

Build agent workflows that take an integration need from plan to implementation and validation with humans approving at the right checkpoints.
Create systems that synthesize context from codebases, docs, specs, telemetry, and historical incidents to make changes that match Datadog conventions and customer expectations.
Generate and evolve integration code and tests, including end-to-end scenarios that reflect real customer workloads and product features.
Design evaluation harnesses that prevent silent regressions: golden sets, scenario baselines, semantic checks, performance thresholds, and release gating.
Build portfolio-level automation: proactive updates for upstream breaking changes, tracer feature rollouts across the catalog, migrations to new schemas/semantics, and targeted coverage expansion.
Partner tightly with PM, support engineers, and integration-owning teams to make the system adoptable, trustworthy, and embedded in daily engineering workflows.

New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
Continuous professional development, product training, and career pathing
Intradepartmental mentor and buddy program for in-house networking
An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
Access to Inclusion Talks, our Internal panel discussions
Free, global mental health benefits for employees and dependents age 6+
Competitive global benefits