What are the responsibilities and job description for the Principal Applied AI Scientist position at Kai?
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
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
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.
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
- 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
- Experience applying AI in cybersecurity, enterprise SaaS, or data-intensive domains
- Background in search or large-scale retrieval systems
- 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