Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about #DatadogLife on Instagram , LinkedIn, and Datadog Learning Center.
At Datadog, we leverage AI across our observability platform to improve monitoring, speed up incident resolution, and ensure data reliability for cloud applications.
Datadog’s Deployment Gates team builds customer-facing systems that decide whether software should ship to production. Deployment Gates sit directly in customers’ CI/CD pipelines and use observability data to answer one of the hardest questions in software delivery:
Given everything we know right now, is this deployment safe to proceed?
Задачи
Work on an analysis service that prevents incidents by detecting faulty software changes in production before they reach clients
Lead the design of progressive deployment automation, starting with zero-setup, conservative AI rules and evolving toward adaptive gates that learn from incidents and organizational patterns
Design the foundation for autonomous remediation, connecting what changed in code to what broke in production, from blocking and rolling back unsafe deployments to proposing fixes and enforcing policies
This is a highly product‑minded engineering role: you’ll work from problem discovery and UX all the way to reliable, scalable production systems
At Datadog, we place value in our office culture - the relationships that it builds, the creativity it brings to the table, and the collaboration of being together
We operate as a hybrid workplace to ensure our employees can create a work-life harmony that best fits them
Build AI-driven deployment gates: Design and ship decision systems that evaluate customer deployments using CI/CD context and Datadog telemetry, producing safe, explainable allow/block outcomes
Own evals and rollout: Define precision, recall, and trust metrics
build offline and online evals
validate changes in shadow mode
and safely promote improvements to enforcement
Design for robustness and safety: Implement conservative defaults, guardrails, fallbacks, and human-in-the-loop paths so gates behave predictably under noisy or incomplete data
Partner closely with Product: Work hand-in-hand with the Product Manager to translate customer problems, adoption signals, and roadmap goals into concrete technical decisions and iterations
Integrate across the Datadog platform: Partner with internal AI teams building the Faulty Deployment Detection pipeline, as well as teams working on LLMs and AI agents
Own production systems: Build and operate reliable backend services that run in the critical path of customer deployments, and be on-call for those services
Требования
A Product‑minded engineer who ships AI to production
You have 5+ years experience with backend systems and microservices performance: tracing, latency breakdowns, concurrency, and resiliency patterns
You are proficient in a modern programming language
strong API/service design
production ops (monitoring, alerting, on‑call rotation)
You have proven experience delivering software based on LLM/agent features to production
You are comfortable owning user journeys, iterating from prototype → alpha → GA, and measuring impact with clear product metrics
An End-to-end AI implementation owner: You understands the end-to-end LLM product lifecycle
Fluent with offline/online evals for AI systems
You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output