AI Backend Engineer Jobs

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As a Software Engineer on Knowledge Systems, you will help build systems that understand what is true about the world by extracting, connecting, retrieving, and reasoning over knowledge from the web and beyond to enable AI agents to answer questions with unprecedented precision and completeness.

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Design and build scalable backend systems powering AI Agents that operate in real-time enterprise environments. Develop agent orchestration frameworks involving multi-step reasoning, tool usage, and decisioning workflows. Build systems for agent memory, context management, and state persistence across interactions. Architect low-latency inference pipelines integrating Large Language Models, Small Language Models, and external tools/services. Implement evaluation frameworks to measure agent performance, accuracy, and reliability. Enable continuous improvement loops for AI agents in production including feedback, retraining, and deployment. Design and manage event-driven, asynchronous workflows for complex agent tasks. Optimize systems for high throughput, low latency, and cost-efficient inference at scale. Build and maintain robust APIs and service layers (REST/gRPC) for agent capabilities. Partner closely with Applied AI/ML teams to productionize models and agent behaviors. Collaborate with Product and Solutions teams to translate real customer workflows into agentic systems. Drive best practices in observability, monitoring, safety, and guardrails for AI systems. Contribute to architecture decisions for scaling multi-tenant, enterprise-grade AI platforms.

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Build core primitives end to end including entity ownership, audit, authorization, and orchestration, ensuring the right actions are the default and incorrect actions are difficult. Own the domain model by turning Fluidstack's concepts of power, datacenters, and chips into composable entities that remain durable over time. Define interactions with external systems by interfacing with vendor systems and ingesting domain-specific formats such as KMZ, BIM, Revit, and vendor documents. Enable AI agents to be first-class operators of Fluidstack's systems by providing tools, guardrails, and audit trails to allow safe and effective operation beyond mere advice.

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Design, develop, and operate backend systems for domain-specific collection search services including news, places, securities, sports, music, and movies. Design and standardize search architectures based on OpenSearch and MongoDB Atlas, including indexing and retrieval structures, to enable rapid expansion of new collections. Analyze search quality and maintain metrics such as nDCG, recall, MRR, and CTR to improve search accuracy, latency, and handle failure cases. Develop and fine-tune ranking models, reranking, embeddings, semantic search, and recommendation logic, focusing on top accuracy for priority collections. Build robust backend infrastructure required for stable production operation, including API contracts, caches, configuration registries, and administrative APIs. Lead technical decision-making processes, conduct design reviews within the team, and address complex problems by improving reusable systems.

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Build backend and infrastructure systems that extend OpenAI's API platform into cloud-native environments such as AWS. Design and ship cloud-contained products that allow customers to use OpenAI capabilities while keeping workloads and data within cloud environments. Help stand up cloud-hosted Codex experiences powered by the OpenAI Responses API. Build infrastructure and runtime abstractions for a stateful, cloud-optimized agentic platform. Partner closely with external cloud partners as well as internal teams across Codex, Research, and Safety Systems to translate emerging capabilities into production-ready systems. Improve the reliability, scalability, observability, and operational maturity of the services underpinning these products. Help shape the technical direction of a new and growing team as it scales from an early core group into a larger engineering organization. This role also involves building backend services, APIs, SDK integrations, authentication flows, and cloud service infrastructure that let developers use OpenAI capabilities in the cloud environments where they already build, and working across teams sometimes embedded with partner product groups to ship products quickly across multiple platforms at the same time.

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Design, build, and operate backend systems that power image generation and image editing experiences in ChatGPT. Develop scalable APIs, services, and infrastructure that support multimodal AI workflows. Optimize reliability, latency, throughput, and cost across large-scale distributed systems. Partner with researchers to productionize new image generation capabilities and bring them to users quickly and safely. Collaborate closely with Android, iOS, web, and full-stack engineers to build seamless end-to-end product experiences. Drive technical architecture decisions across storage, serving, orchestration, and platform systems. Use data and experimentation to identify opportunities for improving user experience, performance, and system efficiency. Help shape engineering culture through technical leadership, mentorship, and operational excellence.

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Build and scale reinforcement learning environments and platforms behind them; drive architecture for scalable, reliable, extensible environment systems and data generation pipelines; partner with Research, Product, and Ops teams to turn ambiguous needs into production systems; build modular, plug-and-play domains that integrate cleanly with training and evaluation loops; improve reliability, observability, performance, and data quality of systems.

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As a backend engineer, you would play a critical role in the search architecture at Exa. Your work may involve building massive-scale machine learning systems, working on projects based on your skills and interests, such as recreating Google-level keyword search over 10 billion pages in one month, building state-of-the-art crawling systems that work optimally for any website, and building custom vector databases that can run over a billion vectors in under 100 milliseconds.

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Design and develop robust, secure, and scalable generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation. Build and optimize high-performance, low-latency APIs and microservices to integrate advanced AI models and sophisticated agentic workflows into the core platform. Make meaningful system design decisions and own the architecture of core platform components from initial proposal through production deployment. Implement and maintain responsive user interfaces using technologies like React and TypeScript to deliver intuitive user experiences and bridge the gap between backend services and frontend enablement. Communicate changes, plans, and proposals clearly to cross-functional teams and collaborate closely with product managers, data scientists, and DevOps engineers. Partner with DevOps teams to build continuous deployment, logging, and monitoring systems that ensure top-tier performance, security, and reliability across distributed workloads.

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The Software Engineer, ML Data Infrastructure will collaborate with engineers to build advanced AI design experiences, tackle complex technical challenges including scaling distributed systems and enabling generative media experiences, build robust data infrastructure at petabyte scale ensuring reliability and performance across multi-modal training pipelines, optimize data processing workflows for high throughput involving distributed systems, TPU infrastructure, and large-scale storage, and partner with research scientists to understand data requirements and translate them into production-grade systems to accelerate model development cycles.