Software Engineer; Backend

Location

Palo Alto, CA

Full time

On-site

Engineering

As a Full Stack Software Engineer, you will be a pivotal force in developing, deploying, and maintaining the end-to-end infrastructure for our advanced AI systems. This includes designing robust backend services, building intuitive and high-performance user interfaces, and ensuring the seamless integration of LLM-based AI Agents. Your expertise will bridge the gap between frontend user experience, backend scalability, and core AI infrastructure, directly impacting system efficiency, reliability, and user-facing capabilities.

• Architect, develop, and maintain scalable full-stack components, including both frontend applications (using modern frameworks like React/Vue/Angular) and robust backend services (leveraging Python/Go/Node.js).
• Design and implement APIs and data pipelines that facilitate the smooth deployment and interaction of sophisticated AI Agents and large-scale data processing workflows.
• Contribute to the development of core AI agent frameworks, focusing on features like tool integration, memory systems, and planning/orchestration modules.
• Develop and implement AI Agent evaluation methodologies and tooling to rigorously test, benchmark, and monitor agent performance, reliability, and safety in production.
• Manage and optimize cloud infrastructure (e.g., AWS, GCP, Azure) to ensure high availability, cost‑efficiency, and scalability for both the application layer and the underlying AI compute resources.
• Participate actively in design discussions, code reviews, and cross‑team collaboration to deliver high‑quality, production‑grade solutions across the entire stack.

• Bachelor’s degree in Computer Science, Computer Engineering, related technical discipline, or equivalent practical experience.
• 3+ years of experience building and maintaining full‑stack software infrastructure, with proven expertise in both frontend and backend development.
• Hands‑on experience building AI agents, AI agent frameworks/orchestration systems, or complex LLM‑powered applications and workflows (e.g., RAG pipelines, multi‑agent systems, prompt chaining architectures, or LLM orchestration frameworks).
• Practical knowledge of cloud infrastructure management (e.g., Docker, Kubernetes, Terraform) and CI/CD pipelines.
• Proven expertise in designing, scaling, and optimizing enterprise‑grade ML or data‑intensive systems.

• Master’s or Ph.D. degree in Computer Science, Computer Engineering, or a related technical discipline.
• Demonstrated experience developing and managing large‑scale distributed systems and high‑throughput AI infrastructures.
• Expertise in a modern frontend framework (e.g., React, Vue, Angular) and associated state management libraries.
• Experience in developing and deploying AI Agent Evaluation frameworks (e.g., using tools like Lang Smith, Arize, or custom evaluation metrics).
• Demonstrated success building production LLM applications with complex workflows such as autonomous agents, conversational AI systems, or intelligent automation platforms.