Nightwing seeks a strong technical leader with experience applying and interacting with LLMs and neural networks across various deployment schemes (e.g., on-prem, cloud hosted, and token-based subscription). This candidate must also have practical leadership experience in the domain of offensive cyber operations (OCO), and vulnerability research. Specifically, this role requires an advanced understanding of genetic search algorithms (e.g., AFL++), coverage-based feedback schemes, modern exploit mitigations (e.g., PAC, MTE), assembly language (e.g., ARM64, Intel x86/x64), and advanced reverse engineering techniques. Nightwing expects successful candidates to be able to bridge these two technical domains, using major advancements in open-weight and SOTA LLMs to drive technical innovation and advancements in the domain of applied offensive cyber tradecraft and capability development.
Core Responsibilities
- Lead a team to design and implement LLM and neural network infrastructure including on-premises deployments, cloud-hosted environments, and token-based API subscriptions for an organization of 300+ engineers.
- Assess and integrate state-of-the-art (SOTA) open-weight models and commercial LLM offerings into OCO tools, actively defeating vendor supplied guardrails.
- Establish processes for rapidly prototyping and evaluating emerging AI capabilities, translating research advancements into new products, pushing these out to a large workforce and iterating their performance.
- Lead development of AI-enhanced fuzzing and vulnerability discovery frameworks leveraging genetic search algorithms (AFL++, libFuzzer, Honggfuzz) coverage guided analysis, source analysis, and exploit development techniques.
Required Qualifications
Technical Expertise
- 5+ years working with LLMs, neural networks, and modern ML frameworks with demonstrated experience across multiple deployment models
- 7+ years in vulnerability research, exploit development, or offensive cyber operations with proven track record of capability delivery
- Expert-level knowledge of genetic search algorithms, coverage-guided fuzzing (AFL++), and automated vulnerability discovery
- Deep understanding of:
- ARM64 and x86/x64 assembly language and microarchitecture
- Modern exploit mitigations and bypass techniques
- Operating system internals (kernel and userspace)
- Memory management and process isolation mechanisms
- Advanced proficiency with tools such as IDA Pro, Ghidra, Binary Ninja, and debuggers (GDB, LLDB, WinDbg)
Additional Requirements
- Education: Bachelor's degree in Computer Science, Computer Engineering, or related field; advanced degree preferred or equivalent practical experience
