Staff Software Engineer

You will drive the development and operations of security services and frameworks that protect Uber's user-facing products and core platforms. This role is ideal for a strong software engineer with a machine learning background who is excited to apply ML/GenAI techniques to real-world security problems at scale.

The security landscape evolves constantly-and with the rise of ML/GenAI and the growing volume of heterogeneous signals (logs, events, graph data, identity signals, endpoints, network telemetry), the attack surface expands just as quickly. You will design and build robust, scalable systems and data pipelines that enable detection, investigation, and automated response-turning noisy telemetry into actionable security insights. You'll collaborate closely with security leadership and partner engineering teams to embed security-by-design across Uber's technology stack.

What the Candidate Will Need / Bonus Points - What the Candidate Will Do -

• Build ML-powered security systems: Design, develop, and operate software and services that improve Uber's security posture, with a focus on detection, classification, and risk scoring.
• Develop backend infrastructure and ETL pipelines: Build reliable data ingestion, transformation, and feature pipelines to support security analytics and machine learning workflows.
• Productionize ML for security use cases: Help take models from experimentation to deployment-owning performance, scalability, monitoring, and model/data quality in production.
• Code review and testing: Maintain high engineering standards through design reviews, code reviews, testing, and operational excellence.
• Cross-functional collaboration: Partner with teams like network operations, incident response, and compliance to ensure cohesive, end-to-end security outcomes.

• BS/MS in Computer Science or a related field
• 7+ years of industry experience in a software development environment
• Proficiency in one or more of Golang, SQL, Python
• Hands-on experience building and operating distributed systems
• Hands-on experience with machine learning (e.g., feature engineering, training/evaluation, or deploying models)
• Experience leading projects with global, cross-functional stakeholders
• Experience mentoring and guiding junior engineers, * Experience with security detection engineering (threat detection, alerting, triage) and/or threat emulation
• Experience with streaming data processing (e.g., Flink)
• Experience with deep learning and/or LLM/GenAI approaches applied to security signals
• Experience with anomaly detection, graph-based detection, or statistical/ML methods for identifying abuse or attacks