We are seeking a talented and motivated Agent OS Developer to join our team in designing and building the core infrastructure that powers intelligent agent-based systems. You will be instrumental in creating the operating system that orchestrates agent behaviors, manages workflows, and integrates with external tools and APIs. This is a unique opportunity to shape the next generation of computing where autonomous agents collaborate to solve complex tasks.

Key Responsibilities

• Design and develop the Agent Operating System (Agent OS) to support multi-agent coordination, task planning, and execution.
• Implement core services such as memory, perception, planning, scheduling, environment modeling, and long-term state persistence.
• Develop abstractions and APIs that allow developers to define, manage, and deploy agents in dynamic environments.
• Integrate with LLMs, vector stores, toolchains, and third-party APIs to enable agent capabilities such as reasoning, code generation, and data analysis.
• Build agent lifecycle management tools, including initialization, state management, failure recovery, and inter-agent communication.
• Ensure the Agent OS is secure, scalable, fault-tolerant, and optimized for low-latency interactions.
• Collaborate with research, ML, and platform teams to test and iterate on features grounded in real-world use cases.
• Contribute to documentation, developer tooling, and best practices for building with Agent OS.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
• 4+ years of experience in systems-level development, distributed systems, or AI frameworks.
• Proficiency in Python, Rust, or Go. Experience with asynchronous programming and microservices architecture.

• Familiarity with agentic frameworks like LangChain, LangGraph, CrewAI, Autogen, ReAct, or bespoke task-oriented agents.
• Familiarity with patterns like Supervisor, Judge and multi-agent architecture
• Deep understanding of AI/ML infrastructure, including LLMs, embeddings, prompt orchestration, and vector databases.
• Experience with multi-agent systems, behavior trees, or reinforcement learning is a plus.
• Knowledge of containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure).

Nice to Have

• Contributions to open-source agent frameworks or AI tooling.
• Experience working with observability and monitoring tools to track agent performance.
• Exposure to knowledge graphs, memory management systems, or retrieval-augmented generation (RAG) pipelines.

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