Senior 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.

About the Role

We are looking for a Senior Applied AI Scientist to design and develop cutting-edge Generative AI and LLM-powered systems for real-world, high-impact applications.

This is a senior, hands-on technical role for someone who can operate across key components of modern AI — from modeling and experimentation to production systems — while contributing to system design and collaborating across teams.

You will work at the intersection of LLMs, agentic systems, retrieval architectures, and scalable AI platforms, building systems that move from prototypes to reliable, production-grade solutions.

Key Responsibilities

• Design and develop large-scale AI/ML and Generative AI systems
• Build and deploy LLM-powered applications, including RAG pipelines and agent-based systems
• Contribute to the architecture and implementation of scalable AI systems
• Collaborate with scientists, engineers, and product teams to deliver end-to-end AI solutions
• Develop retrieval and embedding systems for intelligent applications
• Implement agentic workflows with tool use, memory, and reasoning capabilities
• Support the full model lifecycle: data curation → training/fine-tuning → evaluation → deployment → monitoring
• Translate business requirements into practical AI solutions in collaboration with stakeholders

Required Qualifications

• 4+ years of experience in Applied AI / Machine Learning / Generative AI
• Strong experience building and deploying production-grade AI systems
• Experience working in cross-functional teams to deliver AI solutions
• Ability to communicate technical concepts effectively with product and engineering stakeholders
• Deep expertise in LLMs, RAG, and modern Generative AI systems
• Solid system design skills across data, models, and infrastructure
• Ability to move from experimentation → production deployment
• Strong ownership mindset and ability to operate in fast-paced environments
• Experience with agentic frameworks and tool-based AI systems

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

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