Principal Software Engineer - AI Safety and Security

Lead the design and implementation of shared platform capabilities: guide architecture and technology decisions, establish reusable engineering patterns, write critical production code, and unblock complex technical work across teams. Build and evolve company-wide AI observability and monitoring systems that enable attack reconstruction, automated detection, evaluation, threat hunting, and investigation, using modern agentic development tools and AI-assisted coding workflows to prototype, evaluate, and iterate rapidly. Design detection infrastructure for attack signatures, pattern matching, behavioral anomaly detection, model-assisted analysis, and emerging research techniques. Apply emerging AI security research to detection, evaluation, observability, and safer implementation patterns. Build and operate large-scale distributed data processing systems using technologies such as Spark, Kusto, data lakes, and batch and streaming platforms. Develop high-volume data integrations and correlation pipelines that turn multi-source telemetry into investigation-ready evidence, threat intelligence, trends, and reporting. Drive technical partner conversations and security and privacy architecture reviews, resolve detailed design constraints, and work with teams through integration and implementation. Review designs and code, mentor engineers and scientists, and raise the technical bar across teams. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience, including significant ownership of distributed data, platform, or security products with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 2+ years of experience working on modern AI systems, such as copilots, agent-based products, LLM applications, agent orchestration and tool use, embeddings, vector databases, retrieval-augmented generation, or evaluations. 2+ years of experience big data engineering, such as Spark, Kusto, data lakes, batch and streaming pipelines, data quality, performance tuning, and cost and reliability trade-offs. Demonstrated success designing and delivering complex production systems in ambiguous, cross-functional environments, identifying and addressing security and privacy risks in complex production systems. Proven coding, system design, written communication, and technical leadership skills. End-to-end technical ownership of complex platforms, data, security, or AI products. Hands-on experience with alerting, triage, investigation, threat hunting, or incident response.

leading security and privacy architecture reviews, applying threat-modeling and secure-by-design principles, and translating review requirements into implementable designs.

Deep understanding of agentic systems, tools, memory, embeddings, and AI system failure modes.

with cloud-native distributed platforms, infrastructure as code, CI/CD, and technologies such as Azure Data Factory, Databricks, Kubernetes, or equivalent systems.

Demonstrated record of mentoring and influencing engineers, researchers, product leaders, and partner organizations without direct authority.