AI Backend Engineer Jobs

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Create functional black box tests for large codebases in various source languages. Create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms. Monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards. Leverage LLMs (Roo Code, Claude) to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Create functional black box tests for large codebases in various source languages; create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms; monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards; leverage large language models (LLMs) such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Create functional black box tests for large codebases in various source languages; create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms; monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards; leverage large language models (LLMs) such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Create functional black box tests for large codebases in various source languages. Create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms. Monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards. Leverage LLMs (Roo Code, Claude) to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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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 by deploying, serving, and scaling systems that combine internal research and external capabilities to unlock new product experiences. 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.

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As a Software Engineer at Magic, you will work on core systems or product surfaces that directly determine model capability and user experience. The role includes end-to-end ownership: defining problems, implementing solutions, shipping to production, and iterating based on real outcomes. Responsibilities may include building and scaling large distributed data pipelines for pre-training; designing filtering, mixture, and dataset versioning systems; developing post-training datasets, evaluation frameworks, and reward pipelines; running ablations that translate capability goals into measurable improvements; building end-to-end product surfaces that integrate deeply with the model; designing APIs, backend services, and frontend workflows for AI-first experiences; and improving reliability, observability, and performance of production systems. The position involves working with the unique technical challenges posed by long-context models such as internet-scale data acquisition, long-horizon post-training loops, and product workflows that make complex model behavior understandable and controllable. The role can evolve into specialization in data systems, post-training capability development, or product engineering leadership, depending on strengths and interests.

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Design and implement high-throughput, isolated processing clusters for Enterprise clients ensuring strict tenant isolation and High Availability. Drive improvements across Temporal workflow clusters and production Kubernetes environments with scaling strategies supporting self-serve consumers and Enterprise contracts. Collaborate with the AI/ML team to transform experimental models into scalable, production-ready services, owning infrastructure that connects model outputs to user-facing features with minimal latency. Build and operate high-dimensional vector database infrastructure for semantic search enabling users to find exact moments in video using natural language. Architect backend systems for bulk workflow orchestration, resource isolation, and multi-tenancy to support large media houses managing massive video archives.

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Design, develop, and maintain scalable, high-performance backend systems for an automated order-taking platform. Collaborate closely with the team to ensure seamless integration of backend systems with machine learning models and client devices. Monitor and optimize backend performance in production environments. Build and maintain integrations with third-party restaurant software systems such as POS, loyalty, payment gateways, and customer data platforms. Implement best practices in system design, code quality, and testing to ensure reliability, security, and maintainability. Optimize AI pipelines for better performance in challenging audio environments, handle ambiguous customer requests robustly, and rapidly scale to new menus. Push the boundaries of large language models (LLMs) and voice AI technology. Run experiments to validate the product impact of new functionalities.

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As a Member of Technical Staff, Design at Listen Labs, you will be responsible for building a product that changes how companies make decisions by tackling complex problems end-to-end. You will own a part of the product and make decisions across the LLM pipeline, infrastructure, backend, and UX. The role involves pushing the most advanced AI models to their limits and working with foundational companies on new AI model releases. You will help build and enhance key technical challenges including developing a research AI agent, constructing a comprehensive database of human profiles, enabling realtime video interviews with emotional understanding, creating an agent for distributed information mining, and modeling customer preferences and synthetic personas. The position requires caring deeply about the product quality, obsessing about details, having strong product instincts, and being able to communicate tradeoffs, problems, and blockers directly with minimal meetings.

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Own full lifecycle management of advertising systems, from experimentation through deployment and continuous enhancement. Design, implement, and deploy data-driven algorithms and computational models for intelligent advertising platforms. Build scalable, high-performance software components that enable content personalization for publishers and brands. Monitor and evaluate performance of deployed models to ensure high system reliability. Analyze performance data and implement improvements to optimize accuracy and efficiency. Conduct ongoing research into emerging algorithms and data processing techniques, aligning solutions with latest academic and industry advancements. Proactively integrate new methodologies and technologies to strengthen and expand system capabilities. Perform data wrangling and preprocessing using SQL and related tools to prepare structured training data. Ensure data integrity and usability through best practices in validation, error handling, and resource management.