AWS AI Jobs

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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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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 AI Deployment Engineer serves as the primary technical subject matter expert post-sale for a portfolio of customers, embedding deeply with them to design and deploy Generative AI solutions. They engage with senior business and technical stakeholders to identify, prioritize, and validate the highest-value GenAI applications in customers' roadmaps. The role accelerates customer time to value by providing architectural guidance, building hands-on prototypes, and advising on best practices for scaling solutions in production. The engineer maintains strong relationships with leadership and technical teams to drive adoption, expansion, and successful outcomes. They contribute to open-source resources and enterprise-facing technical documentation to scale best practices across customers. The engineer shares learnings and collaborates with internal teams to inform product development and improve customer outcomes. Additionally, they codify knowledge and operationalize technical success practices to help the Solutions Architecture team scale impact across industries and customer types.

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The AI Deployment Engineer is responsible for serving as the primary technical subject matter expert post-sale for a portfolio of customers, embedding deeply with them to design and deploy Generative AI solutions. They engage with senior business and technical stakeholders to identify, prioritize, and validate high-value GenAI applications in the customers' roadmaps. The role involves accelerating customer time to value by providing architectural guidance, building hands-on prototypes, and advising on best practices for scaling solutions in production. The engineer maintains strong relationships with leadership and technical teams to drive adoption, expansion, and successful outcomes. They contribute to open-source resources and enterprise-facing technical documentation to scale best practices, share learnings, and collaborate with internal teams to inform product development and improve customer outcomes. Additionally, they codify knowledge and operationalize technical success practices to help the Solutions Architecture team scale impact across industries and customer types.

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Build and operate high-throughput pipelines for sensor and event data (batch and streaming) ensuring quality, lineage, and reliability. Create scalable dataset curation and labeling workflows including sampling, slice definitions, weak supervision, gold-set management, and evaluation set integrity. Develop ML platform components such as feature pipelines, training orchestration, model registry, reproducible experiment tracking, and automated evaluation. Implement monitoring and observability for production ML systems covering data drift, performance regression, alerting, and automated failure detection. Standardize schemas and interfaces across studies and product telemetry to enable reusable, consistent analytics and model development. Collaborate cross-functionally with ML engineers, data science, firmware, and backend teams to support new studies and product launches, ensuring data architecture meets evolving research and product needs.

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The Machine Learning Engineer is responsible for building and deploying machine learning models that enhance sleep experiences through personalization, prediction, and behavior understanding, including readiness forecasting, event detection, and individualized recommendations. They will apply and adapt foundation-model capabilities to product workflows, develop user behavior models connecting longitudinal signals to actionable interventions, and design evaluation strategies for offline metrics, slice-based analysis, calibration, reliability, and fairness. The role involves partnering with Product teams to run high-quality online experiments, productionizing models via scalable training and inference pipelines, model monitoring, drift detection, alerting, and continuous improvement loops. Collaboration with cross-functional partners such as Product, Mobile, Backend, and Clinical teams is essential to scope requirements and deliver high-impact features.