Engineering Manager, Machine Learning

Contenido del rol extraído en secciones para revisar más rápido.

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

Have a strong ML engineering background that you use to inform and validate the team's technical decisions, and you're comfortable staying close to the code
Are excited to shape how AI and developer tools come together, and want to define how engineers and coding agents collaborate to find and fix problems
Are driven by impact and want to lead a team working on high-stakes, high-visibility projects
Love building things from the ground up — the ML team is still in its early chapters, and there's plenty of greenfield to shape
Thrive in cross-functional environments and enjoy collaborating across product, design, and engineering to ship great work
Care deeply about growing the engineers around you and finding opportunities for them to do the best work of their careers

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

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