AI Backend Engineer Jobs

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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.

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Build end-to-end product features across frontend, backend, and AI integrations; design agent workflows that handle planning, tool use, failure, and recovery across multiple steps; integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions; design real-time AI interactions with streaming, partial results, and tight latency constraints; improve system reliability, observability, and fallback mechanisms; collaborate closely with ML, backend, and product teams to ship features end-to-end; continuously iterate based on real usage and failure modes.

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As a Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. You build and operate backend systems that serve AI-powered features in production, design inference pipelines, orchestration layers, and service boundaries around models. You are responsible for production concerns such as monitoring, logging, alerting, and incident response. Additionally, you optimize latency and throughput across inference, caching, batching, and streaming. Your work enables backend systems to run reliably at scale, handling production AI traffic with low latency and high throughput, ensuring APIs are stable, clear, and support seamless integration with frontend and ML systems. You ensure production incidents are quickly detected, diagnosed, and resolved, minimizing user impact, and continuously improve system performance and reliability through iterative changes based on real usage.

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Collaborate with the AI Tools squad to implement and improve AI features in the Photoroom app, including Logo maker, AI Images, AI Videos, and other features on the app homepage. Design and architect new features by chaining a mix of internal and external services to generate images and videos for users. Monitor and scale the growing load on the FastAPI service using Datadog to find optimizations and bottlenecks or implement smart caching of pipeline steps.

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Design and build extensible backend systems that support flexible configurations for different customers and content types. Develop infrastructure that interfaces cleanly with large language models (LLMs), enabling prompt engineering, context injection, and modular evaluation workflows. Build tooling and platforms that enable fast iteration by AI engineers and analysts, including declarative pipelines, parameterized jobs, and reproducible experiments. Prioritize ease of deployment, integration, and testing for both internal teams and external partners. Collaborate closely with product, data, and policy teams to translate nuanced safety needs into scalable, maintainable software systems.

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Design, build, and own backend systems end-to-end, including services, APIs, data pipelines, and infrastructure that power the products. Solve complex technical challenges across distributed systems, scaling, concurrency, and performance. Integrate and operate large generative AI models in production, including deploying, serving, and scaling systems that combine internal research and external capabilities. Instrument, experiment, and iterate in production to continuously improve system and product quality. Design and operate core platform infrastructure, including integrations with third-party providers, storage systems, security, and internal APIs.