Sentry
Software Monitoring Tools
EngineeringManager,MachineLearning
Neural analysis suggests this role is
optimal for Manager candidates.
“Engineering Manager, Machine Learning at Sentry. Skills: Machine Learning Engineering Management, Technical Leadership in ML, ML System Design and Deployment, Team Building and Mentorship. Set technical direction across the team's full ML surface area. Define how the team evaluates and monitors ML systems in production”
What You'll Achieve.
Help developers write better software faster; Build the next generation of software monitoring tools; Lead and grow our Machine Learning Engineering team; Ship great work; Find opportunities for engineers to do the best work of their careers
Industry & Context.
Unblock engineers on complex ML problems; Judgment in ambiguous, fast-moving environments
What They're Looking For.
Must Have
8+ years of professional engineering experience, Significant time spent building and shipping machine learning systems in production, 3+ years of engineering management experience, Leading ML, AI, or data-focused teams, Familiarity with deploying and operating ML models at scale, Evaluation, monitoring, and iteration in production, Judgment in ambiguous, fast-moving environments, Excellent written and verbal communication, Comfortable working across product, research, and engineering
Nice to Have
A research background in machine learning, statistics, or a related field (MS, PhD, or equivalent research experience)
What You'll Do.
Set technical direction across the team's full ML surface area
Define how the team evaluates and monitors ML systems in production
Review code and model designs
Contribute to architecture discussions
Unblock engineers on complex ML problems
Define team roadmap and deliverables
Keep execution on track against ambitious goals
Partner with product managers
and engineering leaders
Identify the highest-impact opportunities for ML in our products
Foster career growth for the engineers on your team
Recruit exceptional ML talent as the team scales
How You'll Work.
Team & Collaboration
Partner closely with product, design, and engineering leaders; Work across product, research, and engineering
Communication Scope
Excellent written and verbal communication
Process & Methodology
Define team roadmap and deliverables, Scope work, Allocate resources, Keep execution on track against ambitious goals
Full Job Description
ABOUT SENTRY Bad software is everywhere, and we’re tired of it. Sentry is on a mission to help developers write better software faster so we can get back to enjoying technology. With more than $217 million in funding and 100,000+ organizations that believe we’re on to something, we're building performance and error monitoring tools that help companies like Disney, Microsoft, and Atlassian spend less time fixing bugs and more time building products. Sentry embraces a hybrid work model across our global hubs, with Mondays, Tuesdays, and Thursdays set as in-office anchor days to encourage meaningful collaboration. If you like to selfishly build things that make your digital life better, come help us build the next generation of software monitoring tools. 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, rankin
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