--- title: "Software Engineer (Backend)" description: "About Us: At Nace AI, we are redefining how professional services operate by delivering Sovereign Specialized Intelligence. As an applied research and produc…" canonical: "https://nace.ai/careers/ebfef074-7d3a-4c29-8ac9-d429ef463cb4" updated: 2026-03-17 --- # Software Engineer (Backend) > About Us: At Nace AI, we are redefining how professional services operate by delivering Sovereign Specialized Intelligence. As an applied research and produc… Engineering · Palo Alto, CA · Full-time [Apply for this position](https://jobs.ashbyhq.com/nace.ai/ebfef074-7d3a-4c29-8ac9-d429ef463cb4/application) 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. [Apply for this position](https://jobs.ashbyhq.com/nace.ai/ebfef074-7d3a-4c29-8ac9-d429ef463cb4/application) ## For agents - [Drex API guide](https://nace.ai/drex.md): the decision model: request shape, question types, benchmarks and deployment, as markdown - [Decision endpoint](https://console.nace.ai/v1/systemone): POST with model "drex-latest", a state string and typed questions (choice, noul, score); https://console.nace.ai/v1/models lists checkpoints - [Document endpoints](https://console.nace.ai/v1/documents/{parse|split|classify|extract|ground}): POST a source (a public URL, an uploaded workspace file or a finished parse job) and poll GET https://console.nace.ai/v1/documents/jobs/{id}; the same key and credit as decisions - [Console](https://console.nace.ai): API keys, the playground, usage and billing; send `Authorization: Bearer $NACE_API_KEY` on every request - [Console docs](https://console.nace.ai/docs): quickstart, question types, the document routes, errors, limits and pricing - [Nace Document Intelligence](https://nace.ai/perception.md): the Perception series: parse, split, classify, extract and ground any file type with a location on every value - [Drex for Search](https://nace.ai/drex/search.md): Drex for Search: decision-powered retrieval for agents, Drex finds the page, cited search through the CLI, SDKs, MCP and REST, and the public benchmark ranks - [Use cases](https://nace.ai/use-cases.md): every Drex use case with its proof figures: browser use and search for developers, enterprise finance teams and professional firms - [Developers](https://nace.ai/developers.md): The execution layer for agents: Perception, Decision and Search behind one key, the Drex Max frontier, install steps, a scripted agent session and the four deployment boundaries - [Site index](https://nace.ai/llms.txt): every page on nace.ai; append `.md` to any page URL or send `Accept: text/markdown` for the markdown version