Principal Applied AI Scientist

Kai is the AI company rebuilding cybersecurity for the machine-speed era. Founded by second time founders and trusted by Fortune 500 enterprises, Kai is building a future where security has no categories, no silos, and no human speed bottlenecks. The Kai Agentic AI Platform replaces fragmented, human-limited workflows with agentic AI systems that continuously contextualize, assess, reason, and execute security work at machine speed - making human defenders, superhuman.

Why Join Kai

  • Well-funded: With $125M raised, we have the capital, runway, and resolve to rebuild cybersecurity from first principles.
  • Proven: We've earned the trust of Fortune 500 and Global 1000 companies, and we're just getting started. Their confidence in Kai reflects what we've built: an AI-powered cybersecurity platform that performs at the scale and speed the enterprise demands.
  • Experienced founders: Our founding team consists of second-time entrepreneurs, each with over 20 years of experience in the cybersecurity industry. Their proven expertise and vision drive our ambitious goals.
  • World-class leadership team: Our Heads of AI, Engineering, and Product bring extensive experience from some of the world’s most influential companies, ensuring top-tier mentorship, direction, and vision.
  • Frontier AI Applied Research Team: Our researchers operate at the leading edge of agentic AI systems, translating breakthrough capabilities into real-world cybersecurity applications.
  • Generous compensation: We offer highly competitive salaries, equity options, and a supportive work environment. Your contributions will be valued and rewarded as we grow together.

We are looking for a Principal Applied AI Scientist to lead the design and deployment of cutting-edge Generative AI and LLM-powered systems for real-world, high-impact applications. 

This is a senior, hands-on leadership role for someone who can operate across the full stack of modern AI — from research and modeling to production systems and productization — while leading teams and defining technical direction. 

You will work at the intersection of LLMs, agentic systems, retrieval architectures, and large-scale AI platforms, building systems that move beyond prototypes into robust, production-grade intelligence systems. 


What You’ll Do 

  • Lead the end-to-end design and development of large-scale AI/ML and Generative AI systems  
  • Architect and deploy LLM-powered applications, including RAG pipelines and multi-agent systems  
  • Drive the technical vision and roadmap for applied AI across the company  
  • Build and lead a high-performing team of scientists and engineers  
  • Design scalable retrieval and embedding systems powering intelligent applications  
  • Develop agentic AI systems with tool use, memory, and reasoning capabilities  
  • Own model lifecycle: data curation → training/fine-tuning → evaluation → deployment → monitoring  
  • Partner with product, engineering, and executive leadership to translate business problems into AI solutions  


Required Qualifications 

  • 7+ years of experience in Applied AI / Machine Learning / Generative AI  
  • Proven experience building and deploying production-grade AI systems at scale  
  • Demonstrated leadership managing large cross-functional teams  
  • Strong experience engaging with executives and product stakeholders 
  • Deep expertise in LLMs, RAG, and agentic AI systems at scale  
  • Strong system design skills across data, models, and infrastructure  
  • Ability to move from research ideas → production systems → business impact  
  • Strong ownership mindset with the ability to operate in fast-moving startup environments  
  • Experience with multi-agent orchestration frameworks and tool ecosystems  


Preferred Qualifications 

  • Experience applying AI in cybersecurity, enterprise SaaS, or data-intensive domains  
  • Background in search or large-scale retrieval systems  


Core Technical Expertise 

  • Large Language Models & GenAI 
  • RAG, Retrieval & Vector Systems 
  • Fine-Tuning & Model Adaptation 
  • Agentic AI Systems 
  • Prompt Engineering & Optimization 
  • Evaluation & Quality 
  • Inference & Serving 
  • Data Engineering & Synthetic Data 
  • Multimodal AI 
  • LLMOps & Observability 
  • Platforms & Infrastructure 

 

AI Research

San Jose, CA

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