Meet the Team

We are an agile team with a startup feel and a strong bias for action. We move fast, embrace failure as part of the process, and stay focused on solving real-world problems for defenders on the front lines. Our team blends deep expertise in AI, cybersecurity, platform and systems engineering. We are driven by a shared belief that the only way to outpace hackers is through AI advancements that free up humans to tackle real threats and more challenging problems.

This is a place for builders who thrive in ambiguity, challenge the status quo, and care deeply about making a meaningful impact. If you’re energized by tough problems, excited to shape the future of cyber defense, and eager to work alongside passionate experts, you’ll feel right at home.

Your Impact

• Design and build systems that combine security-relevant data, detection signals, context, and foundation models to spot and predict threats, those in action and those that are about to happen.

• Prototype and test new AI features - from decoding events and system environments to predicting anomalies and threats - working closely with security SMEs and security researchers to validate real-world utility.

• Develop an AIDevOps pipeline to enable rapid experimentation with data. Knowledge bases, models, and context memory, using clear, measurable success criteria to evaluate iterations.

• Architect and build the distributed platform that can correlate trillions of events & signals, and can synthesize them into explainable findings and alerts, consumed by our triage resolution agents – you will be building a key capability of the Agentic SOC.

• Collaborate with product and platform teams to co-design AI-enhanced threat detection and prediction workflows that are intuitive, scalable, and immediately useful to analysts.

• Contribute to the core architecture powering AI-native security operations, helping to shape how Splunk and Cisco scale trusted automation across the enterprise.

Minimum Qualifications

• Bachelor's Degree with 12+ years of related experience or Master's with 8+ years of related experience.

Security:

• Security Operations Experience – Understanding of security operations concepts, including attack surface management, threat analytics, detection, triage, investigation, and response.

• Security Telemetry Fluency – Comfortable working with common data sources such as endpoint logs, network traffic, authentication events, or cloud audit trails—and understanding how they’re used in detection and investigation workflows.

Engineering Experience:

• Data Platform/Fabric Familiarity – Experience implementing data platforms and/or data lakes and warehouses.

• Security Data Engineering – Experience building and maintaining pipelines for ingesting, parsing, and normalizing large-scale security telemetry.

• Distributed Systems design and implementation expertise – with an emphasis on storage systems and storage access layers

• Senior-Level Python Development – Consistent record building scalable backend services, APIs, and automation workflows in Python.

• DevOps/SecOps Practices – Proficient with CI/CD pipelines, version control (GitHub/GitLab), Jira, and automated testing frameworks.

• Agentic development – Experience designing agent systems that will perform coding tasks for you.

• Cross-Functional Collaboration – Experience partnering with product managers, security SMEs, and engineers to iterate quickly and deliver impactful solutions.

Preferred Qualifications

• Enterprise-grade Security Product Experience – Familiarity with TDIR/SIEM architectures, correlation searches, threat hunting support systems. Bonus if you’ve worked with Splunk’s APIs, internals, or have experience developing on the Splunk platform.

• Security Operations Background – Former Tier 3 SOC analyst or equivalent, with experience automating SecOps workflows and building scalable, resilient detection infrastructure.

• Data Encoding & Embeddings – Exposure to event storage systems or generating custom embeddings for domain-specific tasks in cybersecurity.

• RAG and GraphRAG Search Implementation – Hands-on experience developing retrieval-augmented generation pipelines and working with databases (e.g., FAISS, Pinecone).

• Model Integration – Skilled in crafting, testing, and optimizing training workloads for large language or special-purpose models such as GNNs, GCNs, GATs. Ideally, you have contributed to or shipped an AI-powered feature or product, and understand the nuances of integrating models into real-world workflows - including usability, performance, and trust considerations.

• AI Evaluation & Experimentation – Capable of designing experiments to evaluate model output for accuracy, usability, performance, and cost

• UX and Human Factors for Analysts – Background or interest in designing intuitive, AI-assisted analyst workflows with a focus on usability, trust, and decision support.