Senior Staff Software Engineer - Observo AI

Sentinel Labs
United States - RemotePosted 27 February 2026

Job Description

<div class="content-intro"><h3>About Us</h3> <p>At SentinelOne, we’re redefining cybersecurity by pushing the limits of what’s possible—leveraging AI-powered, data-driven innovation to stay ahead of tomorrow’s threats.</p> <p>From building industry-leading products to cultivating an exceptional company culture, our core values guide everything we do. We’re looking for passionate individuals who thrive in collaborative environments and are eager to drive impact. If you’re excited about solving complex challenges in bold, innovative ways, we’d love to connect with you.</p></div><h2><strong>What Are We Looking For?</strong></h2> <p>SentinelOne is seeking a <strong>Senior Staff Software Engineer</strong> to join the Observo.ai team, our cutting-edge AI-driven data pipeline optimization platform. This role will be responsible for leading the architectural design and technical strategy for high-performance systems that process massive volumes of telemetry data while reducing costs and improving insights for enterprise customers. The ideal candidate brings extensive expertise in distributed systems, data engineering, and machine learning infrastructure, with a proven track record of driving technical excellence and leading complex engineering initiatives at scale. This role is part of the Observo.ai engineering organization and will require partial on-site presence at our Mountain View, CA headquarters.</p> <h2><strong>What Will You Do?</strong></h2> <ul> <li>Lead the architectural design and technical roadmap for scalable, high-performance data processing pipelines capable of handling petabyte-scale telemetry data (logs, metrics, traces)</li> <li>Drive the development and optimization of ML-driven data routing, filtering, and transformation engines to reduce customer data volumes by 80%+ while preserving critical insights</li> <li>Architect and implement real-time analytics and anomaly detection systems using advanced machine learning techniques and large language models</li> <li>Design cloud-native microservices and APIs that integrate seamlessly with major observability platforms (Splunk, Elastic, Datadog, New Relic)</li> <li>Establish robust monitoring, alerting, and observability solutions for distributed systems operating at enterprise scale</li> <li>Lead cross-functional technical initiatives, collaborating with Product, Data Science, and DevOps teams to translate strategic vision into technical solutions</li> <li>Drive system performance, cost efficiency, and reliability optimization through advanced profiling, testing, and infrastructure design</li> <li>Provide technical leadership and mentorship to senior and junior engineers, establishing engineering best practices and culture</li> <li>Evaluate and introduce emerging technologies in AI/ML, data engineering, and observability to maintain competitive advantage</li> <li>Participate in technical decision-making forums and contribute to company-wide engineering standards and practices</li> </ul> <h2><strong>What Skills and Knowledge Should You Bring?</strong></h2> <ul> <li><strong>10+ years of software engineering experience</strong> with a focus on distributed systems, data engineering, or ML infrastructure in high-growth SaaS environments</li> <li><strong>Expert-level proficiency</strong> in Go, Rust, or Java with a deep understanding of system design patterns, software architecture principles, and performance optimization</li> <li><strong>Extensive experience</strong> with cloud platforms (AWS, GCP, Azure) and container orchestration technologies (Kubernetes, Docker) at enterprise scale</li> <li><strong>Proven track record</strong> leading and scaling data pipelines using technologies like Apache Kafka, Apache Spark, Apache Flink, or similar streaming frameworks</li> <li><strong>Deep expertise</strong> in database technologies, including both SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, Redis) systems with experience in data modeling and optimization</li> <li><stro ... (truncated, view full listing at source)