2389 وظيفة Offensive Cyberspace Operations Planner قريبًا مني

Nightwing provides technically advanced full-spectrum cyber, data operations, systems integration and intelligence mission support services to meet our customers’ most demanding challenges. Our capabilities include cyber space operations, cyber defense and resiliency, vulnerability research, ubiquitous technical surveillance, data intelligence, lifecycle mission enablement, and software modernization. Nightwing brings disruptive technologies, agility, and competitive offerings to customers in the intelligence community, defense, civil, and commercial markets.

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

At Nightwing, we value collaboration and teamwork. You’ll have the opportunity to work alongside talented individuals who are passionate about what they do. Together, we’ll leverage our collective expertise to drive innovation, solve complex problems, and deliver exceptional results for our clients.

Thank you for considering joining us as we embark on this new journey and shape the future of cybersecurity and intelligence together as part of the Nightwing team.

Nightwing is An Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status, age or any other federally protected class.