AWS AI Jobs

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Design, deploy, and maintain Figure's training clusters. Architect and maintain scalable deep learning frameworks for training on massive robot datasets. Work together with AI researchers to implement training of new model architectures at a large scale. Implement distributed training and parallelization strategies to reduce model development cycles. Implement tooling for data processing, model experimentation, and continuous integration.

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Set the technical direction for complex machine learning projects, balancing trade-offs and guiding team priorities. Design, implement, and maintain reliable, scalable ML and software systems while justifying key architectural decisions. Define project problems, develop roadmaps, and oversee delivery across multiple workstreams in often ill-defined, high-risk environments. Drive the development of shared resources and libraries across the organisation and guide other engineers in contributing to them. Lead hiring processes, make informed selection decisions, and mentor multiple individuals to foster team growth. Proactively develop and execute recommendations for adopting new technologies and changing ways of working to stay competitive. Act as a technical expert and coach for customers, accurately estimate large workstreams, and defend rationale to stakeholders.

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Design and ship production systems around models, owning integrations, data flows, reliability, and on-call readiness. Lead discovery and scoping from pre-sales through post-sales, including problem framing, constraints, trade-offs, and a delivery plan. Define launch criteria for regulated contexts, outcome metrics, and drive adoption until production impact is proven. Build in sensitive data environments where auditability, validation, and access controls drive architecture decisions. Run evaluation loops that measure model and system quality in life science workflows to drive model and product improvements. Distill production learnings into hardened primitives, reference architectures, and templated workflows that scale across regulated life sciences environments.

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As a Software Engineer on the Monetization Delivery team, you will architect, build, and optimize the ads delivery platform that serves ads in real time at global scale. You will design systems aimed at ensuring correctness, safety, and continuous optimization under heavy load. Your work will involve developing decision engines, ranking logic, and delivery pipelines that require strong performance, reliability, and privacy guarantees. You will partner with Product, Design, and Research teams to define requirements and drive novel solutions to highly complex technical problems. The role includes prototyping, experimenting, and deploying rapid iterations to improve delivery quality, relevance, and efficiency. Additionally, you will implement infrastructure and services that support ongoing measurement, optimization, and model-based improvements, ensuring operational excellence through monitoring, alerting, performance analysis, and rigorous testing. You will contribute to technical strategy and help shape the long-term roadmap of the ads delivery stack while building safety, fairness, and policy alignment into delivery systems from first principles.

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Design, build, and operate scalable back-end systems that power AI agent and workflow builders. Own mission-critical services and infrastructure, delivering impactful features from ideation through to production. Push the boundaries of applied AI by enabling new agent capabilities, workflow orchestration, and system behaviours. Shape how we build by influencing engineering standards, architecture, and processes as we scale. Mentor and support engineers across the team, raising the bar for technical quality and ownership. Set and uphold high standards for code quality, performance, reliability, and security. Collaborate closely with product, design, and leadership to align technical direction with business outcomes.

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Design, build, and operate scalable back-end systems that power real-time, AI-driven chat experiences. Own mission-critical services and infrastructure, delivering impactful features from ideation through to production. Push the boundaries of applied AI by enabling new agent capabilities, workflows, and system behaviours. Shape how the engineering team builds by influencing engineering standards, architecture, and processes as the team scales. Mentor and support engineers across the team, raising the bar for technical quality and ownership. Set and uphold high standards for code quality, performance, reliability, and security. Collaborate closely with product, design, and leadership to align technical direction with business outcomes.

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Design, build, and operate scalable back-end systems that power real-time, AI-driven chat experiences. Own mission-critical services and infrastructure, delivering impactful features from ideation through to production. Push the boundaries of applied AI by enabling new agent capabilities, workflows, and system behaviours. Shape how engineering standards, architecture, and processes are built and scaled. Mentor and support engineers across the team, raising the bar for technical quality and ownership. Set and uphold high standards for code quality, performance, reliability, and security. Collaborate closely with product, design, and leadership to align technical direction with business outcomes.

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Design, build, and operate scalable back-end systems that power AI agent and workflow builders. Own mission-critical services and infrastructure, delivering impactful features from ideation through to production. Push the boundaries of applied AI by enabling new agent capabilities, workflow orchestration, and system behaviours. Shape engineering standards, architecture, and processes as the team scales. Mentor and support engineers across the team, raising the bar for technical quality and ownership. Set and uphold high standards for code quality, performance, reliability, and security. Collaborate closely with product, design, and leadership to align technical direction with business outcomes.

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Design and implement AI agents and extend existing agents with new capabilities including managing agent context using techniques like sub-agents and retrieval-based context management. Develop complex tools for agents such as computer use and browser use. Research and develop multi-agent orchestration and tool calling systems to enable collaboration between agents. Build and maintain production-grade APIs and AI-powered features across backend services and user-facing experiences. Evaluate AI performance through tests and evaluations and iterate on prompts, agent tools, and orchestration to improve output quality and reliability.

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Develop AI agents and multi-agent systems by designing and implementing agents and extending existing agent capabilities, including managing the agent's context using techniques like sub-agents and retrieval-based context management. Develop complex tools for agents such as computer use and browser use. Research and build systems for multi-agent orchestration and agent tool calling to manage collaboration and tool usage. Engage in full-stack development to build and maintain production-grade APIs and AI-powered features, covering backend services to user-facing experiences. Evaluate AI performance using tests and evaluations to iterate and improve prompts, agent tools, and orchestration for better output quality and reliability.