Principal Software Engineer

As a Principal Software Engineer, AI-SOC, your primary responsibility will be to drive the technical design and delivery of the foundational systems that make autonomous investigation possible at scale.

Specifically, your focus will be to:

• Architect and implement core platform components across the AI-SOC stack, including data ingestion pipelines, query engines, investigation orchestration, and ML model integration layers.

• Own technical direction for the team's most complex and ambiguous problems, defining interfaces, data contracts, and system boundaries that scale reliably across diverse alert types and log sources.

• Partner with ML/AI engineers to design the infrastructure that trains, serves, and evaluates disposition models, ensuring tight feedback loops between production signals and model improvement.

• Establish and champion engineering standards across the team: system design patterns, data modeling conventions, testing practices, and observability requirements.

• Lead design reviews and provide deep technical mentorship to senior and staff engineers, raising the overall technical ceiling of the team.

• Identify and resolve systemic performance, reliability, and scalability bottlenecks across the distributed investigation pipeline.

• Collaborate with Product Management to shape requirements, surface technical constraints early, and propose solutions that balance speed with long-term platform health.

• Contribute to cross-team architectural decisions and represent AI-SOC engineering in broader platform and infrastructure forums.

The skills and qualities you'll bring include:

• Bring 10+ years of software engineering experience, with a demonstrated track record of leading complex, high-scale system design across distributed environments.

• Demonstrate deep expertise in data systems, including stream processing (e.g., Kafka, Flink), distributed query engines, data modeling, and storage systems (e.g., DynamoDB, S3, data mesh patterns).

• Apply strong working knowledge of ML/AI system design, including model serving infrastructure, feature pipelines, embedding systems, or LLM integration. You don't need to be a data scientist, but you need to build confidently alongside them.

• Proven experience designing systems where correctness, latency, and throughput all matter. You think carefully about trade-offs and can articulate them clearly.

• Excel at taking ownership of ambiguous, cross-cutting technical problems and driving them to well-reasoned, well-documented solutions.