Software Engineer (Backend)
nace.ai · Palo Alto, CA ·
- Work mode
- Onsite
- Category
- Backend
- Experience
- 3+ years
nace.ai · Palo Alto, CA ·
About Us:
At Nace AI, we are redefining how professional services operate by delivering Sovereign Specialized Intelligence. As an applied research and product company, we equip enterprises with a comprehensive AI stack to build customized, secure intelligence tailored to their unique business needs.
Driven by advanced Small Language Models and our dynamic metamodel framework, our flagship platforms - Nace Data Intelligence and the Nace SLM Cloud - enable true end-to-end business process automation. The result is transformative ROI: professional services firms using Nace AI are currently recovering 1,000 hours per client engagement, drastically reducing overhead and accelerating delivery.
The work we are doing has a meaningful impact across industries, and every hire at Nace AI plays a critical role in shaping the company’s trajectory. This is a unique opportunity to join a high conviction AI company at an early stage and directly influence its growth.
If building a world-class AI team from the ground up excites you, we’d love to talk.
Role Overview:
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.
What You'll Do:
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.
Minimum Qualifications:
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.
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 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 LangSmith, 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.