Applied Scientist

Applied Scientist - AI Operating System (Agentic AI startup) Join us at NinjaTech AI as an AI Operating System Applied Scientist, reporting directly to the co‑founder / Chief Science and Technology Officer. Shape the future of autonomous AI agents by designing the operating systems that power the next generation of intelligent software. About us NinjaTech AI is a generative AI startup (B2C and B2B) headquartered in Silicon Valley with offices in Sydney and Vancouver. Our Engineering team is largely ex‑AWS and our Product and UX team is ex‑Google. Backed by Alexa Fund and Samsung Ventures, we are on track to raise Series A funding in … Why join NinjaTech AI This is a unique opportunity for a motivated Applied Scientist to help build the foundation of next‑generation AI agent systems. Unlike typical AI / ML roles focused on model development, this position focuses on the critical infrastructure that will enable autonomous agents to operate effectively at scale. What makes you a strong match for this Science position Research publications in systems for AI, virtualization, file systems, or related areas Experience translating research concepts into production‑ready implementations Contributions to open‑source AI projects or research communities Experience building distributed systems at scale Knowledge of vector databases and embedding techniques Hands‑on experience with AWS Firecracker or other microVM implementations Expertise in designing and implementing fast file systems Experience with cloud‑native security architectures and compliance frameworks Background in audit logging systems and security monitoring for AI applications Knowledge of performance optimization for virtualized environments in cloud settings Experience with secure multi‑tenancy in AI systems Key challenges you will work on Just as traditional operating systems manage resources, schedule tasks, and provide interfaces for applications, our AI Operating System will serve as the foundation for autonomous AI agents. We're creating a system that enables: Virtual Context Management: allowing AI agents to effectively utilize memory beyond their limited context windows Resource Orchestration: intelligently managing computational resources, memory, and storage for AI agents Tool Integration: providing standardized interfaces for agents to interact with external tools and APIs Agent Collaboration: facilitating communication and coordination between specialized AI agents Persistent Memory: enabling agents to maintain and manage long‑term memory across sessions Secure Isolation: implementing microVM‑based isolation for secure and efficient agent execution High‑Performance Storage: leveraging optimized file systems for AI workloads and memory management Comprehensive Auditing: maintaining transparent records of all agent actions and resource usage Experience Requirements 1+ years of industry experience (can be before or after PhD) PhD in Computer Science, Artificial Intelligence, or a related field Strong research background demonstrated through publications or patents Familiarity with LLMs and their limitations Proficiency in Python and at least one systems programming language (C++, Rust, Go) Knowledge of memory management techniques and optimization Familiarity with AI agent frameworks and architectures Knowledge of high‑performance file systems and storage optimization techniques Understanding of cloud‑based virtualization architectures and containerization Background in security principles including isolation and access controls Excellent problem‑solving skills and ability to work on complex systems Day‑to‑day responsibilities Advanced Memory Management Systems: research and develop novel approaches for AI agent memory hierarchies that effectively utilize different storage tiers and context management techniques MicroVM Isolation Architectures: investigate state‑of‑the‑art approaches to secure agent execution using Firecracker and other microVM technologies, focusing on performance optimization and security guarantees High‑Performance AI File Systems: research and develop specialized file systems optimized for AI workloads, with particular focus on fast access patterns, efficient vector storage, and memory‑mapped approaches Cloud‑Native Virtualization: explore novel architectures for deploying AI agents in cloud environments with optimal resource utilization and scaling capabilities Security and Auditing Frameworks: develop comprehensive approaches to AI system governance, including transparent logging, monitoring, and compliance verification Multi‑Agent Collaboration Protocols: design and implement efficient communication mechanisms between isolated agent instances Resource Optimization Algorithms: create adaptive systems that intelligently allocate computational resources based on agent workloads and priorities Benefits Annual health insurance subsidy Superannuation Paid Time Off (Vacation, Sick & Holidays) Paid lunches when you work on‑site Stock Option Plan #J-18808-Ljbffr