AI Applied Engineer

As an AI Applied Engineer at Radiant Security, you'll develop AI-driven solutions that enhance security operations for our clients. This involves working on agentic workflows, optimizing LLMs, and participating in proof-of-concept projects. Your role is vital in addressing key challenges in accuracy and scalability, ensuring our automated systems effectively triage security alerts.

To excel as an AI Applied Engineer at Radiant Security, you should possess a strong background in AI applications, with experience in NLP and machine learning algorithms. Familiarity with LLMs and retrieval-augmented generation (RAG) is a plus. Being a great communicator and a proactive problem-solver will enhance your success in this role.

In the role of an AI Applied Engineer at Radiant Security, you will work with cutting-edge technologies, including large language models (LLMs), big data integrations, and various machine learning algorithms. You'll also collaborate on innovative projects that leverage AI to solve real-world cybersecurity challenges.

Radiant Security offers a competitive benefits package for AI Applied Engineers, including a generous equity package, unlimited paid time off, excellent healthcare benefits, and top-of-market salaries. We believe in creating a supportive work environment that values the well-being and growth of our team.

The interview process for the AI Applied Engineer role at Radiant Security is designed for efficiency and thorough evaluation. It includes an executive interview followed by a technical interview, and a virtual on-site where candidates engage in both technical and leadership discussions, ensuring a good fit for both the candidate and our company culture.

When answering this question, highlight a specific project related to AI that showcases your problem-solving skills, technologies used, and how your actions led to a successful outcome. Focus on your role and any quantifiable impacts your contributions made.

Discuss your experience with large language models in detail, mentioning any practical applications you've worked on, how you fine-tuned models, and the results achieved. This will demonstrate your depth of knowledge and hands-on experience in this area.

Outline your methodology for optimizing AI algorithms, including specific techniques you've used, any relevant metrics for measuring performance, and examples of the improvements achieved through your optimizations.

Share your perspective on emerging trends in AI and cybersecurity, showing that you stay informed about industry developments. Discuss what excites you most and how you see AI continuing to evolve in this field.

Provide a clear and concise explanation of retrieval-augmented generation, highlighting its role in enhancing data retrieval during AI processing. Explain how it can improve the performance and accuracy of AI models in practical applications.

Answer by discussing methods you use for maintaining transparency in AI models, such as documenting the decision-making processes, explaining output reasoning, and any tools that help visualize or explain AI model behavior.

Share your experiences collaborating with teams on AI projects, including how you communicate effectively, address challenges, and celebrate successes. Emphasize the value of teamwork in achieving project goals.

Mention specific tools and platforms you are familiar with, such as TensorFlow, PyTorch, or custom solutions. Elaborate on your experiences using these tools in various stages of AI development and how they contributed to your project successes.

Discuss your approach to receiving and implementing feedback on your AI models, emphasizing the importance of collaboration, continuous improvement, and being open to different perspectives in refining your work.

Reflect on the importance of ethics in AI development, discussing how you address ethical considerations in your work. Highlight your commitment to building fair and transparent AI systems that respect user privacy and rights.