Senior AI/ML Engineer

Excerpt from the original posting

PagerDuty, Inc. (NYSE: PD) is the global leader in AI-first digital operations. By automatically detecting, diagnosing, and remediating issues, the PagerDuty Platform orchestrates AI agents and automated workflows with context from over 750 integrations. Trusted by approximately two-thirds of the Fortune 100 and nearly half of the Fortune 500, PagerDuty is the industry standard for organizations scaling resilient, autonomous operations. Notable customers include Chipotle, Cloudflare, Docusign, Fox, Nvidia, Salesforce, Spotify, Zoom and more. We are growing rapidly and hiring top talent with leading AI skills across engineering, sales, product, marketing, and beyond as we build the leading digital operations platform.

 

About the role

PagerDuty’s Operations Cloud runs on a platform that ingests billions of signals and turns them into real-time action for thousands of customers. We’re looking for a Senior AI/ML Engineer who lives at the intersection of two disciplines: large-scale distributed systems and applied AI.

In this role you will design and ship AI systems that run in production at PagerDuty’s scale — powering Incident Management AI Agents, event intelligence, and the LLM-powered capabilities embedded across our platform. You’ll own the full lifecycle, from framing the problem to serving reliably at scale.

We are looking for a candidate who is genuinely passionate about building with modern AI — LLMs, agents, and retrieval — but grounded in the realities of building resilient, high-throughput systems.

What you’ll do

- Design and build AI-powered features — LLM agents, retrieval, and event intelligence — that operate on high-volume, real-time event streams, from problem framing through production deployment and monitoring.

- Architect and own the systems behind them: agent and prompt orchestration, retrieval pipelines, tool/API integrations, and low-latency inference and evaluation at scale.

- Reason about consistency, throughput, fault tolerance, and cost across services that must stay reliable under bursty, unpredictable load.

- Take AI features from prototype to production, establishing the evaluation, guardrail, observability, and improvement loops that keep them accurate and trustworthy over time.

- Partner with platform, product, and applied-research teams to define what “good” looks like and to integrate AI cleanly into existing services.

- Raise the bar through example, reviews and mentorship, and help shape the team’s technic…

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