AI Research Scientist
Posted 24 days ago · 87 applicants
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The role in plain words
This role focuses on applying artificial intelligence and large language models to cyber intelligence and vulnerability research. You will rapidly experiment with new technologies, hack and dissect systems, and translate research ideas into working code. The day-to-day work involves solving complex technical challenges to help build and shape novel AI capabilities.
- Proven experience in Vulnerability Research and Exploitation.
- Ability to rapidly learn new technologies
- Experience building, evaluating, or applying LLMs to complex technical problems, or a strong desire and ability to rapidly develop expertise in this domain
- Ability to turn research ideas into practical, impactful solutions
- Understanding of the internals of LLMs, their low-level implementation and training algorithms
Extracted from the job description · kept up to date automatically
Who this suits
This role suits individuals with proven experience in vulnerability research and exploitation who either have experience with LLMs or a strong desire to learn them. It is less ideal for those without a background in vulnerability research or those who prefer working on established, non-experimental technologies.
Full job description
Original listing · kept for referenceDescription
Paragon is on a mission to transform the world of cyber intelligence. At our company you’ll find the freedom to think boldly, collaborate with purpose, and grow alongside some of the best researchers in the industry, united by a shared mission.
We are looking for highly motivated candidates to join our exceptionally talented and passionate vulnerability research group that constantly challenges the boundaries of what’s possible. In this role, you will have the opportunity to take part in world-class frontier research, solve incredibly complex and satisfying technological challenges, contribute to the development of revolutionary AI capabilities and help shape the future of AI-driven cyber intelligence.
Responsibilities
Passionate: care deeply about technology challenges, strive for excellence, determined to make a meaningful impact.
Curious: learn rapidly and eagerly; ask “why?” and “what if?” a lot.
Innovative: think outside the box, challenge prevailing assumptions, create new ideas.
Tenacious: relentlessly focused and determined, try many approaches and never give up
Collaborative: understand that truly great achievements come from great teamwork, enjoy collaborating with others and are eager to share the journey with us.
Hands-on: able to hack and dissect new technology, rapidly experiment, implement and verify ideas, and translate them to working code.
Excited about AI: eager to learn and explore the new technologies, apply AI to solve unique research problems and help create and shape novel AI capabilities within a world-class research organization.
Requirements
• Proven experience in Vulnerability Research and Exploitation.
• Ability to rapidly learn new technologies
• Experience building, evaluating, or applying LLMs to complex technical problems, or a strong desire and ability to rapidly develop expertise in this domain
• Ability to turn research ideas into practical, impactful solutions
• Understanding of the internals of LLMs, their low-level implementation and training algorithms
• Experience with frontier research, solving novel problems - an advantage
• Experience with Reinforcement Learning - an advantage
• Experience in Reverse Engineering, OS and compiler internals - an advantage
• Experience with CTFs - an advantage
Questions about this role
- This listing did not state a salary. We only show pay when the employer publishes it.
- Proven experience in Vulnerability Research and Exploitation., Ability to rapidly learn new technologies, Experience building, evaluating, or applying LLMs to complex technical problems, or a strong desire and ability to rapidly develop expertise in this domain, Ability to turn research ideas into practical, impactful solutions, Understanding of the internals of LLMs, their low-level implementation and training algorithms