Docker AI Jobs

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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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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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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.

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Own the evaluation stack by building online and offline evaluation pipelines that measure agent quality across ephemeral, voluminous MELT data, code, and unstructured documents, and set metrics defining the experience. Define quality at scale by designing evaluations that capture trajectory quality in production incidents spanning hundreds of services with ephemeral, high-volume, and approximative ground truth, ensuring metrics predict real outcomes. Build platform abstractions for agents by designing core agent architectures and extending internal frameworks such as sub-agents, MCPs, and middleware to enable confident iteration and faster shipping with product, platform, and research teams. Productionize these systems by owning latency, observability, and uptime.

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The Evaluation Engineer will own the technical foundation of the auto-evaluation systems by building a comprehensive system that runs fast, is easy to use, and supports quickly building new evaluations. Responsibilities include improving the speed of the basic evals infrastructure with minimal latency, designing interfaces suitable for ML engineers, product managers, and customers, and ensuring the system architecture allows team members to easily add examples and run evaluations. The role also involves ensuring evaluations are accurate and reliable by encoding knowledge about how pharma customers make decisions, providing appropriate statistical tests, and confidence intervals for trustworthy results. Additionally, the engineer is expected to spend most time on the core eval platform, collaborate with the evals team on specific evals, mentor an evals engineering intern, and learn how users interact with the evaluation system to improve it.

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The role involves translating AI research into product solutions by working with client-side researchers on post-training, evaluations, safety, and alignment, and building necessary primitives, data, and tooling. The candidate will partner deeply with core customers and frontier research labs to address complex technical problems related to model improvement, performance, and deployment. They are expected to shape and propose model improvement work by translating customer and research objectives into clear proposals and execution plans. Responsibilities include leading the end-to-end lifecycle from discovery through shipping initial solutions and scaling pilots, independently managing technical working sessions with senior stakeholders, defining success metrics, surfacing risks, and driving programs to measurable outcomes. The role requires cross-functional collaboration with research, platform, operations, security, and finance teams to deliver production-grade results. Additionally, the candidate will build robust evaluation frameworks, close the loop with data quality and feedback, and share learnings to enhance execution across accounts.