Engineering Manager, Machine Learning

Sentry•San Francisco, CA

7d•$220,000 - $280,000•Hybrid

About The Position

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.

Requirements

8+ years of professional engineering experience, with significant time spent building and shipping machine learning systems in production
3+ years of engineering management experience, ideally leading ML, AI, or data-focused teams
Familiarity with deploying and operating ML models at scale, including evaluation, monitoring, and iteration in production
Strong judgment in ambiguous, fast-moving environments
Excellent written and verbal communication; comfortable working across product, research, and engineering

Nice To Haves

A research background in machine learning, statistics, or a related field (MS, PhD, or equivalent research experience) is a plus but not a requirement

Responsibilities

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
Define team roadmap and deliverables, scope work, allocate resources, and keep execution on track against ambitious goals
Partner with product managers, designers, and engineering leaders across Sentry to identify the highest-impact opportunities for ML in our products
Foster career growth for the engineers on your team, and recruit exceptional ML talent as the team scales