Engineering Manager, Machine Learning

Sentry Sentry · Enterprise · San Francisco, CA · Engineering

Engineering Manager to lead and grow a Machine Learning Engineering team at Sentry. The team owns the full spectrum of ML at Sentry, from classical techniques to LLM-based and agentic systems. The role involves setting technical direction, defining evaluation and monitoring strategies, staying hands-on, defining team roadmap, partnering with product and engineering leaders, and fostering career growth and recruiting talent.

What you'd actually do

  1. 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
  2. Define how the team evaluates and monitors ML systems in production, from offline metrics to online experimentation to model and agent observability
  3. Stay hands-on enough to review code and model designs, contribute to architecture discussions, and unblock engineers on complex ML problems
  4. Define team roadmap and deliverables, scope work, allocate resources, and keep execution on track against ambitious goals
  5. Partner with product managers, designers, and engineering leaders across Sentry to identify the highest-impact opportunities for ML in our products

Skills

Required

  • 8+ years of professional engineering experience
  • 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 have

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

What the JD emphasized

  • building and shipping machine learning systems in production
  • deploying and operating ML models at scale
  • evaluation, monitoring, and iteration in production
  • LLM-based and agentic systems
  • agent observability

Other signals

  • AI-native future
  • LLM-based and agentic systems
  • classical techniques like clustering, ranking, anomaly detection, and embeddings
  • proposes a fix