Hands‑on role focused on rapidly developing ML solutions with cross‑functional teams. Work alongside cyber analysts on cyber data to deliver models for object detection, data triage, search/optimization, inference, behavior detection, and automated discovery and decision making. Maintain robust model versioning and improve model resilience by identifying emerging vulnerabilities.

• Architect, train, and deploy AI systems for reasoning, planning, and multi‑step decision‑making.
• Build and scale distributed training/inference pipelines using Ray and modern cloud infrastructure.
• Integrate agentic AI into operational environments across cross‑functional teams.
• Prototype techniques from memory‑augmented models to tool‑use orchestration.
• Develop ML models on cyber data to support object detection, data triage, search/optimization, inference, behavior detection, and automated discovery and decision making.
• Partner with cyber analysts and SMEs to translate operational needs into deployable models and services.
• Maintain a robust model versioning and experiment tracking system.
• Identify new vulnerabilities and failure modes in models; implement defenses, monitoring, and continual evaluation.
• Influence strategy and the future of intelligent autonomous systems.

• 4+ years building ML solutions at scale.
• Strong Python; deep experience with PyTorch/TensorFlow.
• Hands‑on distributed systems experience (Ray highly valued).
• Understanding of LLM orchestration, memory, retrieval, and planning.
• Experience with MLOps/model versioning and experiment tracking (e.g., MLflow, DVC, Weights & Biases).
• Experience working with cyber/network telemetry or security datasets is a plus.
• Familiarity with model security/adversarial ML; ability to identify and mitigate vulnerabilities.
• Active TS/SCI clearance; builder’s mindset.

Analyze binaries, reverse‑engineer protocols, and collaborate with ML to bring intelligence to system analysis.

• Reverse‑engineer binaries, firmware, and network protocols.
• Use AI to accelerate static/dynamic analysis and detect patterns.
• Design RE pipelines that integrate ML insights.
• Collaborate with engineers and researchers on AI+RE solutions.

• 1–3 years RE or low‑level systems experience.
• Familiarity with Ghidra, IDA Pro, or Binary Ninja.
• Scripting in Python; knack for automation.
• Interest in leveraging AI for RE; detail‑oriented mindset.

Not a coffee‑run internship — work on real deliverables at the intersection of AI and reverse engineering.

• Assist with development, training, and deployment of ML models.
• Support RE projects from code analysis to automation.
• Prototype tools that bridge AI and system analysis.
• Learn from engineers working at the cutting edge of AI and cybersecurity.

• Experience with Python and interest in ML and/or RE.
• Desire to learn, experiment, and solve problems.
• Collaborative mindset and curiosity about how systems work.