Okay, I've searched for Senior AI Researcher roles in the San Francisco Bay Area within cybersecurity, offering hybrid work arrangements, requiring skills in LLMs and NLP, and full-time schedules. I have found 134 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

The Senior AI Researcher role at Radiant Security is fundamentally aimed at architecting and advancing the intelligent core of a cutting-edge cybersecurity product that automates alert triage and investigation. This position demands more than just technical competence; it requires a visionary approach to embedding scalable, accurate, and transparent AI-driven decision-making flows into security operations. Success here hinges on balancing bleeding-edge theoretical research with pragmatic, cost-effective application, especially integrating large language models (LLMs) and retrieval-augmented generation (RAG) techniques in diverse customer environments. The job involves leading complex research projects from proof of concept through production deployment while collaborating closely with both technical and non-technical teammates. Candidates must be problem solvers who can navigate the intricacies of cybersecurity alerts, AI interpretability, and scalability challenges, all under a startup’s fast-paced, resource-conscious environment. Success in the first 6–12 months would mean delivering improved, deployable AI agentic workflows that significantly enhance alert triage accuracy, reduce false positives, and meet diverse customer infrastructure constraints.

Company Analysis:

Radiant Security operates at the intersection of AI innovation and cybersecurity, specifically targeting enterprise customers overwhelmed by massive alert volumes. As a nimble and well-funded startup in the competitive Bay Area ecosystem, Radiant’s market positioning is both disruptive and specialized—a company rewriting the traditional SOC playbook by dynamically handling all alerts to minimize manual analyst work and costs. This creates a workplace culture that is fast-paced, research-driven, and impact-oriented, valuing hands-on tech expertise and agility over rigid hierarchies. The company’s mission to reduce response times from days to minutes through adaptive AI strongly aligns with the Senior AI Researcher's role of building intelligent, agentic AI flows. Teams are likely small and cross-functional, with a premium placed on clear communication and proactive collaboration, positioning this role as a highly visible, strategic contributor to product and company growth. The hybrid work model reinforces a culture that blends autonomy with strong team engagement. A candidate successful here would thrive by embracing innovation, dealing with ambiguity, and directly influencing product-market fit and customer success.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to lead a complex AI research project from a proof-of-concept phase to production deployment. What challenges did you face and how did you overcome them?
• Behavioral: Describe an experience where you had to communicate highly technical AI concepts to non-technical stakeholders. How did you ensure clarity and alignment?
• Behavioral: Share an example of when you had to balance trade-offs between AI model accuracy and system cost or scalability. What factors influenced your decisions?
• Behavioral: Give an example of how you handled a situation where a research approach you led initially failed to meet expected outcomes. What did you learn?
• Technical: How would you approach designing and optimizing agentic AI workflows to triage security alerts accurately while maintaining transparency?
• Technical: Explain your experience with retrieval-augmented generation (RAG) and how you would implement it to improve AI consistency in a security operational context.
• Technical: Discuss your methods for fine-tuning large language models specifically for cybersecurity applications. What metrics or criteria do you prioritize?
• Technical: What strategies would you use to ensure AI solutions can operate effectively across varied and potentially resource-constrained customer environments?
• Situational: Imagine you deploy an AI triage model that performs well in testing but produces unexpected false negatives in live customer data. How would you investigate and remediate this issue?
• Situational: Suppose your team is divided between prioritizing cutting-edge ML research and delivering stable, scalable production solutions. How would you help align the team towards a shared goal?
• Situational: You’re asked to build a PoC using a novel AI technique for alert investigation with tight deadlines. How do you prioritize features and ensure meaningful results?
• Company Fit: What about Radiant Security’s mission and product offering excites you most, and how do you see your skills contributing to that vision?
• Company Fit: How do you think working in a startup environment, especially one focused on cyber AI innovation, differs from more established companies? How do you adapt your work style accordingly?
• Company Fit: Radiant emphasizes preventing analyst burnout by automating triage comprehensively. How does this philosophy resonate with your approach to AI in cybersecurity?
• Questions for Interviewer: How does Radiant prioritize research initiatives against immediate product feature demands, especially for AI-driven capabilities?
• Questions for Interviewer: Can you describe the team structure the Senior AI Researcher will collaborate with, and what leadership or mentorship opportunities exist?
• Questions for Interviewer: What are the biggest technical challenges Radiant anticipates in scaling AI triage across diverse enterprise environments over the next year?