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Engineering Manager, Machine Learning

Sentry · San Francisco, California

RemoteManagerData and MLEngineeringEngineeringPosted 2 months ago
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About this role

ABOUT SENTRY Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. ABOUT THE ROLE AI and machine learning are reshaping how developers debug, monitor, and ship software, and Sentry is uniquely positioned to lead that shift. We sit on a novel and massive dataset of real production errors, spans, and logs from tens of thousands of engineering organizations - the kind of signal that makes ML genuinely useful, whether it's a clustering model that groups related issues, a ranking system that surfaces the right alert at the right time, or an agent that proposes a fix. We're looking for an Engineering Manager to lead and grow our Machine Learning Engineering team. This team owns the full spectrum of ML at Sentry: classical techniques like clustering, ranking, anomaly detection, and embeddings that quietly power core product surfaces today, alongside the LLM-based and agentic systems shaping where the product is headed. You'll partner closely with product, design, and engineering leaders to decide where ML belongs in our products, what kind of ML actually fits the problem, and how we translate that work into experiences millions of developers rely on every day. IN THIS ROLE YOU WILL - Set technical direction across the team's full ML surface area - from classical models for clustering, ranking, and anomaly detection to LLM-based and agentic systems - and make sharp calls about which approach fits each problem - Define how the team evaluates and monitors ML systems in production, from offline metrics to online experimentation to model and agent observability - Stay hands-on enough to review code and model designs, contribute to architecture discussions, and unblock engineers on complex ML problems -

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