Engineering Manager, Machine Learning

📋 Description

• • Lead and grow Sentry’s Machine Learning Engineering team, owning the full spectrum of ML initiatives from classical techniques like clustering, ranking, and anomaly detection to LLM-based and agentic systems that power core product features.
• • Set the technical direction for ML systems, making strategic decisions on which approaches—classical models or generative AI—are best suited for specific product problems based on data, scalability, and impact.
• • Define and implement robust evaluation, monitoring, and observability practices for ML models and agents in production, including offline metrics, online A/B testing, and real-time performance tracking.
• • Stay technically hands-on by reviewing code and model architectures, contributing to system design discussions, and unblocking engineers on complex ML challenges.
• • Partner with product, design, and engineering leaders to identify high-impact opportunities for machine learning across Sentry’s application monitoring platform, ensuring ML solutions align with user needs and engineering workflows.
• • Define and own the team’s roadmap, including scoping deliverables, allocating resources, and ensuring execution against ambitious product goals aligned with Sentry’s AI-native future.
• • Recruit exceptional ML engineering talent as the team scales, building a diverse and high-performing team capable of delivering production-grade ML systems at scale.
• • Foster career growth for ML engineers by providing mentorship, setting clear development goals, and creating opportunities for them to lead technical initiatives and solve high-stakes problems.
• • Collaborate cross-functionally to translate ML research into user-facing features that millions of developers rely on daily, ensuring seamless integration into Sentry’s monitoring and debugging experience.
• • Ensure ML systems are not only accurate but also reliable, interpretable, and maintainable in production environments with strict uptime and performance requirements.
• • Drive innovation in how AI agents and coding assistants collaborate with developers to automatically detect, triage, and propose fixes for application errors and performance issues.
• • Balance rapid experimentation with production-grade engineering standards, ensuring that early-stage ML prototypes are rigorously validated before deployment.
• • Maintain deep familiarity with the unique dataset of production errors, spans, and logs from tens of thousands of engineering organizations, leveraging this signal to train and refine ML models.
• • Champion a culture of ownership, curiosity, and technical excellence within the ML team, encouraging iterative improvement and data-driven decision-making.
• • Communicate complex ML concepts clearly to non-technical stakeholders, including product managers and designers, to ensure alignment on goals and constraints.
• • Contribute to the long-term vision of making Sentry the standard for AI-native application monitoring by pioneering the next generation of developer-focused AI tools.