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