AI Software Engineer Jobs

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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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Build end-to-end features across backend and frontend that directly impact customer revenue. Work closely with design, product, and GTM to ship features from idea to production. Translate real-world workflows, including calls, scheduling, and dispatch, into scalable systems. Improve system reliability, performance, and developer velocity as the system scales. Learn fast by working directly with customers and iterating based on real usage.

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Build production AI workflows including agentic workflows over enterprise and government data with clear rules for model visibility, tool usage, and human review. Design context and grounding systems for models to provide relevant information without violating permissions or performance constraints. Work across backend services, APIs, async workers, data pipelines, internal tools, and product-facing surfaces. Engineer reliable LLM systems by building evaluations and feedback loops for model behavior and workflow outcomes. Own tracing and runtime visibility across models, context, tool calls, and generated outputs. Debug failures using context, traces, tool responses, user reviews, and production logs. Improve quality without compromising latency, cost, or security. Build reusable AI systems by creating shared primitives for context assembly, grounding, tool use, and reviewable outputs. Develop systems that turn domain-specific AI behavior into product infrastructure rather than one-off customer logic. Move quickly from prototype to production quality systems with founders and engineers. Lead through ownership and engineering quality by taking ownership of important product and platform surfaces without needing heavy direction. Write clean, maintainable code and create clear abstractions. Use tools like Claude Code, Codex, ChatGPT, Cursor, and similar to accelerate development while maintaining the same standards for generated and hand-written code. Treat LLMs as architectural components with failure modes and costs to manage, not just black boxes to call.

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Design, build, and launch end-to-end product experiences for image generation and image editing within ChatGPT. Develop highly interactive frontend experiences that make sophisticated AI capabilities feel intuitive, fast, and delightful. Build scalable backend services, APIs, and workflows that power image creation, editing, storage, sharing, and retrieval. Partner closely with researchers to rapidly prototype and productionize new multimodal capabilities. Collaborate with Product, Design, Data Science, and Engineering teams to identify high-impact opportunities and execute against them. Own projects from concept through launch, including technical design, implementation, experimentation, measurement, and iteration. Optimize performance across the stack, from frontend responsiveness and rendering to backend latency, reliability, and scalability. Design systems that can support millions of users generating and interacting with visual content simultaneously. Leverage experimentation and user insights to improve engagement, usability, quality, and product outcomes. Contribute to engineering best practices around architecture, testing, observability, developer productivity, and operational excellence. Help define the future roadmap for AI-powered creative tools and visual experiences.

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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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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will own the production outcome, taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will ensure full-stack integrity by overseeing the health of the platform, ensuring seamless integration between the AI core and all full-stack components from APIs to UI. Additionally, you will build automated systems to monitor model performance and data drift across geographically dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead the response for production issues in mission-critical environments, translate deep technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to ensure field lessons influence future technical architecture and decisions.

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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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In this role, you will build the next set of AI Features at Pylon, rapidly iterating based on customer feedback, and improve the quality and performance of AI features.

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Build the platforms that power Pylon's AI features such as prompt executions and search infrastructure. Improve LLM observability including AI evaluations both online and offline, scorers, and prepare Pylon's AI for future scaling. Enhance the quality and performance of AI features.