Kubernetes AI Jobs

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Oversee the design and operation of the core platform including third-party providers, storage, billing, observability, security, and API. Provide technical leadership for various product and platform features. Improve developer experience to enable the whole team to ship faster. Guide efforts that bridge AI research to production across all modalities such as video, audio, image, and text. Understand the capabilities and limitations of state-of-the-art AI models and leverage them in products. Partner with product, design, and research teams to ensure development aligns with user needs and business objectives.

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Own the technical foundation of Meeno end-to-end including web, mobile, backend, data, and experimentation. Co-design product vision in close partnership with Meeno's team. Build core AI product primitives such as voice capture/playback, low-latency interactions, scene framework (content, branching, scoring hooks), feedback loops and user progression, and personalization. Architect systems for speed and iteration with weekly experiments rather than quarterly releases. Set the engineering standards for quality, reliability, security/privacy, and shipping culture. Hire and mentor engineers as the team scales, focusing on quality over quantity and leveraging AI and talent to maintain lean operations.

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Design and own the semantic layer that powers the system-of-record flywheel, enabling compounding AI products across teams. Build primitives, abstractions, and APIs for product teams to use as building blocks, ensuring ease of use for shipping AI-driven features. Partner closely with internal product and engineering teams to understand needs, eliminate friction, and design intuitive, well-documented systems that are hard to misuse. Architect systems that span data warehouses, OLTP databases, streaming systems, and vector stores, making tradeoffs based on latency, throughput, consistency, and access patterns. Work with leadership to define the long-term platform architecture, including build-vs-buy decisions, evolving the semantic layer, and scaling the system as product surface area grows.

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The Site Reliability Engineer is responsible for designing and operating reliable managed AI services focused on serving and scaling large language model workloads. They build automation and reliability tooling to support distributed AI pipelines and inference services, define, measure, and improve SLIs/SLOs across AI workloads to ensure performance and reliability, and collaborate with AI, platform, and infrastructure teams to optimize large-scale training and inference clusters. Additionally, they automate observability by building telemetry and performance tuning strategies for latency-sensitive AI services, investigate and resolve reliability issues in distributed AI systems using telemetry, logs, and profiling, and contribute to the architecture of next-generation distributed systems designed specifically for AI-first environments.

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Ship critical infrastructure managing real-world logistics and financial data for large enterprises. Own the why by building deep context through customer calls and understanding Loop's value to customers, pushing back on requirements if better solutions exist. Work full-stack across system boundaries including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to handle routine tasks enabling focus on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise velocity.

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Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if there is a better, faster way to solve problems. Work with full-stack proficiency across system boundaries, from frontend UX to LLM agents, database schema, and event infrastructures. Leverage AI tools to handle the boilerplate work so focus can be on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.

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Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs. Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior. Improve observability, reliability, and performance across OpenAI's training platform. Debug and resolve issues in complex, high-throughput distributed systems. Collaborate with systems, infrastructure, and research teams to evolve platform capabilities. Extend and adapt failure detection systems or tracing systems to support new training paradigms and workloads.

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Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training jobs. Develop tooling to identify slow, faulty, or misbehaving nodes and provide actionable visibility into system behavior. Improve observability, reliability, and performance across OpenAI's training platform. Debug and resolve issues in complex, high-throughput distributed systems. Collaborate with systems, infrastructure, and research teams to evolve platform capabilities. Extend and adapt failure detection systems or tracing systems to support new training paradigms and workloads.

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As a Full Stack Software Engineer at Replicant, you will design and deliver technology that powers natural, human-like conversations at scale to help companies reduce wait times, improve customer satisfaction, and empower representatives to focus on complex problems. You will build rich user experiences and backend services that enable customers to design, launch, and monitor AI-powered conversations. Responsibilities include building new features for Replicant's core AI voice and chat products handling millions of daily conversations, shipping full stack end-to-end features quickly, integrating automatic speech recognition, text to speech, and conversational AI model improvements into products, refactoring, optimizing, and debugging production systems balancing latency, cost, and user experience, participating in regular on-call rotations monitoring live systems, continuously improving systems based on performance metrics and customer feedback, shaping a culture emphasizing knowledge sharing and mentorship across distributed systems and enterprise-scale AI design, and participating in team and company-wide office events with travel required.

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Design, develop, and maintain web applications and backend services that integrate ML-powered features. Collaborate closely with Machine Learning Engineers and Product Managers to understand ML system requirements and translate them into robust software solutions. Build reliable, scalable, and low-latency services that support ML inference, data workflows, and AI-driven user experiences. Use LLMs to build scalable and reliable AI agents. Own the full software development lifecycle: design, implementation, testing, deployment, monitoring, and maintenance. Ensure high standards for code quality, testing, observability, and operational excellence. Troubleshoot production issues and participate in on-call or support rotations when needed. Mentor junior engineers and contribute to technical best practices across teams. Act as a strong cross-functional partner between product, engineering, and ML teams.