Software Engineer (Backend)
Department: Engineering
Location: Palo Alto, CA
Employment Type: FullTime
Role Overview
As a Backend Software Engineer, you'll design, develop, and maintain robust backend infrastructure that powers our scalable AI systems, supporting advanced LLM-based AI Agents, large-scale data processing, distributed workflows, and high-performance computing. Your contributions will directly impact system efficiency, reliability, and scalability, enabling cutting-edge AI solutions.
What You’ll Do:
Architect, develop, and maintain scalable backend components that power sophisticated AI-driven workflows and large-scale distributed systems.
Ensure system performance, stability, and scalability through proactive optimization and continuous improvement.
Participate actively in design discussions, code reviews, and cross-team collaboration to deliver high-quality, production-grade solutions.
Continuously analyze and refine infrastructure efficiency, specifically targeting compute resources, storage management, and network optimization.
Minimum Qualifications:
Bachelor's degree in Computer Science, Computer Engineering, related technical discipline, or equivalent practical experience.
3+ years of experience building and maintaining backend infrastructure
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 like LangChain, AutoGPT, or similar).
Proven expertise in designing, scaling, and optimizing enterprise-grade ML or data-intensive systems.
Preferred Qualifications:
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, data pipelines, or high-throughput AI infrastructures.
Experience developing core components of AI agent frameworks, including memory systems, tool integration, planning modules, or agent communication protocols.
Demonstrated success building production LLM applications with complex workflows such as autonomous agents, conversational AI systems, or intelligent automation platforms.
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