AI Backend Engineer Jobs

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Lead the design and implementation of a scalable, high-availability voice infrastructure that replaces legacy systems. Build and refine multi-threaded server frameworks capable of handling thousands of concurrent, real-time audio streams with minimal jitter and latency. Deploy robust ASR > LLM > TTS pipelines that process thousands of calls concurrently. Develop robust logic for handling media streams, ensuring seamless audio data flow between clients and machine learning models. Build advanced monitoring and load-testing tools specifically designed to simulate high-concurrency voice traffic. Partner with Speech Scientists and Research Engineers to integrate state-of-the-art models into a production-ready environment.

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The backend developer will own major feature development and work directly with founders on product development from end to end. Responsibilities include working with a small interdisciplinary team across hardware, software, and design to build new products from scratch; building new features and designing new architecture to address challenging problems; building backend infrastructure to perform scalable training in the cloud; rethinking and refactoring existing codebases for scale; and continuously improving and maintaining code in production. The role involves full ownership throughout the entire product lifecycle, including idea generation, design, prototyping, execution, and shipping, contributing to multiple parts of the codebase in various programming languages.

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The responsibilities include shaping the evolution of Codex by identifying how teams use and break AI-powered software engineering, driving changes across product, infrastructure, and model behavior to improve reliability. Building core team and enterprise primitives to enable Codex usability at scale, such as container orchestration, virtual machine provisioning/configuration, execution sandboxes, shared block storage, RBAC, admin and audit surfaces, usage and pricing controls, managed configuration and constraints, and analytics for visibility into Codex usage. Designing and owning secure, observable, full-stack systems that power Codex across web, IDEs, CLI, and CI/CD, integrating with enterprise identity and governance systems (SSO/SAML/OIDC, SCIM, policy enforcement), and developing data-access patterns that are performant, compliant, and trustworthy. Leading real-world deployments and launches by working with customers and go-to-market teams to roll out Codex across teams, using live usage and operational signals to iterate and improve the product and platform based on real-world feedback.

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Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) that is empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.

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Take part in building a platform used by Data Scientists and Simulation Engineers to build, train and deploy Deep Physics Models. Work on a focused, stream-aligned and cross-functional team (back-end, front-end, design) that is empowered to make its implementation decisions towards meeting its objectives. Gather and leverage domain knowledge and experience from the Data Scientists and Simulation Engineers using your product.

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

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As a Software Engineer on the Codex for Teams team, you will be responsible for shaping the evolution of Codex by identifying how teams actually use and sometimes break AI-powered software engineering tools, driving changes across product, infrastructure, and model behavior to make Codex a reliable teammate for organizations. You will build core team and enterprise primitives that enable Codex to scale, including role-based access control (RBAC), admin and audit surfaces, usage and rate limits, pricing controls, managed configuration and constraints, and analytics for deep visibility into Codex usage. You will design and own secure, observable, full-stack systems that power Codex across web, IDEs, CLI, and CI/CD environments, integrating with enterprise identity and governance systems (SSO/SAML/OIDC, SCIM, policy enforcement) and developing data-access patterns that are performant, compliant, and trustworthy. The role involves leading real-world deployments and launches by working directly with customers and the Go To Market team to roll out Codex, using live usage and operational feedback to rapidly iterate and improve the product and platform capabilities. This position owns systems end-to-end, from architecture and implementation to production operations, emphasizing quality and velocity.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability, and design and implement solutions to address the highest priority issues.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to their customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability, and then design and implement solutions to address the highest priority issues.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a variety of use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues.