Software Development Engineer role focused on building an Agent platform using Generative AI and ML technologies for Amazon Ads. The role involves designing and developing scalable systems for personalized ad experiences, optimizing for cost and latency, and pioneering new approaches to conversational AI. The team owns the SPB Agent, which powers reasoning behind agentic experiences in Ads Console, Sales, and Seller Central.

The Applied Scientist II role on the Brand Registry team at Amazon focuses on designing, developing, and deploying AI solutions, specifically leveraging LLMs and agentic AI frameworks to create intelligent automation and autonomous outcomes for brand protection and seller experience. The role involves owning the end-to-end ML lifecycle, from problem formulation to production deployment, and collaborating with product managers and engineering teams.

This Principal Software Engineer role focuses on building agentic AI solutions for AWS DevOps, aiming to accelerate incident response and improve operational efficiency for production systems. The role involves working with information retrieval systems, knowledge graphs, and LLMs to create a frontier agent that resolves incidents and learns from them for systemic improvements.

This role focuses on building AI/ML solutions for the Ad Sales business, specifically creating customer-facing recommendations and enhancing end-to-end workflows with Generative AI. The scientist will leverage quantitative modeling techniques like Sequential Recommender Systems, Deep Learning, and Reinforcement Learning, and use NLP and Generative AI for explainability. The role involves research, model development, A/B testing, and collaboration with engineering and product teams to deliver production-ready solutions.

The role involves developing and scaling a deep learning compiler stack for AWS Machine Learning accelerators (Inferentia and Trainium chips). The engineer will architect and implement features for the AWS Neuron SDK, focusing on making LLM and Vision models run performantly on accelerators. This includes compiler development, optimization, and integration with ML frameworks like PyTorch, TensorFlow, and JAX.

Senior Manager of Applied Science for Alexa Connections, leading a team to build LLM-powered communication features for millions of users. Focuses on end-to-end applied science projects, from ideation to deployment and monitoring, with an emphasis on scaling ML models and AGI/LLM systems for consumer applications.

Manager for an Applied Science team focused on innovating AI features for Ring and Blink cameras, combining computer vision and generative AI for home security. The role involves leading the team in developing and productizing advanced CV and GenAI models, driving technical strategy for privacy-preserving solutions, and ensuring delivery of high-quality science artifacts for customer-facing products.

The RBKS AI team at Amazon is seeking Applied Scientists to innovate AI features for Ring and Blink cameras, focusing on the intersection of computer vision, generative AI, and ambient intelligence. The role involves productizing research into advanced computer vision and multimodal GenAI models for video understanding, object detection, and real-time applications, with an emphasis on privacy-preserving, efficient fine-tuning, and on-device/in-cloud inference. The goal is to ship AI solutions that enhance home security for millions of customers.

AWS Professional Services is seeking an AI/ML Architect to help Canadian Public Sector clients adopt and implement ML and Generative AI solutions. The role involves understanding business challenges, designing and building ML/GenAI solutions using AWS services, and collaborating with engineering teams to bring them to production.

Senior AI/ML Architect role within AWS Professional Services, focusing on building and deploying ML and Generative AI solutions for Canadian Public Sector clients. The role involves understanding business needs, designing solutions using AWS ML services, collaborating with engineering teams, and supporting projects from inception to production.

Software Development Engineer role focused on productizing Generative AI experiences for advertisers at Amazon Advertising. The role involves building customer-facing features, designing and deploying scalable distributed services, and working with various AI technologies like diffusion models, LLMs, computer vision, and reinforcement learning. It requires strong full-stack coding expertise and an understanding of AI agent concepts.

Manage and mentor a team of scientists and engineers focused on applying AI/ML, including language models, for tax classification and calculation within Amazon's global Tax Engine platform. The role involves improving team processes, balancing experimentation with delivery, and partnering with stakeholders to build roadmaps for new products and services, with a focus on predictive and generative AI applications.

Software Development Engineer II role focused on building an AI-native framework for customer experience engineering within Amazon Prime. The role involves designing and developing Agentcore infrastructure, multi-agent orchestration, and autonomous experimentation capabilities, aiming to compress CX development from weeks to hours using GenAI.

Senior AI/ML Architect role within AWS Professional Services, focusing on building and deploying ML and Generative AI solutions for Canadian Public Sector clients. This involves understanding business needs, designing solutions using AWS ML services, collaborating with engineering teams, and supporting projects from inception to production.

Software Development Manager leading a team to build next-generation offer diagnostic and insight capabilities, focusing on AI agents and autonomous shopping experiences within Amazon's large-scale catalog systems. The role involves defining the agentic diagnostic paradigm and owning critical Tier-1 aggregation services.

Technical Program Manager for AWS PACE team, focusing on accelerating AI innovation for customers by building Generative AI and Agentic AI prototypes. The role involves end-to-end ownership of customer engagements, partnering with architects and designers, and working with technologies like LLMs, RAG, and autonomous agents. The TPM will apply Amazon's Working Backwards methodology, manage risks, and ensure responsible AI practices and production readiness.

This role focuses on designing and building machine learning solutions to enhance customer awareness and product discovery for Amazon's Private Brands. It involves end-to-end project management from ideation to launch, utilizing methods like NLP, deep learning, reinforcement learning, and causal inference. The scientist will work closely with engineering and business teams to solve complex problems and drive measurable business impact.

Software Development Engineer role focused on integrating Generative AI and LLM capabilities into Alexa's audio experiences. The role involves designing and implementing software, creating infrastructure for LLMs in audio, developing tools to evaluate and improve model accuracy, reducing latency, and influencing operational excellence for audio services. It also includes mentoring junior engineers and contributing to hiring efforts.

Manager of AI Platform Engineering to lead a team building and operating data pipelines, models, and platform infrastructure for analytics, science, and AI initiatives. The role involves owning delivery and operational health, mentoring a team, and evolving self-service offerings towards agent-driven approaches. The team will build multi-agent solutions to automate data engineering and BI tasks, and develop/deploy agents for analytics use cases like monitoring and anomaly detection. Collaboration with business intelligence, applied science, and product teams is key.

Senior Applied Scientist role focused on developing and productionizing media planning models and solutions for Amazon Ads. The role involves leveraging various ML techniques, including deep learning, generative AI, causal inference, NLP, and computer vision, to provide measurement insights and optimize advertising outcomes. It requires collaboration with engineering, product management, and business teams, and includes mentoring junior scientists and engaging with the scientific community.

This role focuses on developing and implementing innovative media planning and measurement solutions within Amazon Ads. It involves creating end-to-end ML/DL models, from inception to production, to provide insights and recommendations for advertisers. The role requires a strong foundation in ML/DL, experience with productionizing models, and the ability to work with large datasets and collaborate with cross-functional teams.

Economist role focused on leading research, econometric modeling, and analysis to understand customer preferences and inform business operations. This involves developing analytic tools and economic models, including generative AI agents, to determine customer promise by considering inventory, fulfillment, carrier capabilities, customer preferences, and economic impacts. The role requires designing, building, and validating GenAI agents against traditional econometric models and production experiments, with the goal of driving changes in transportation and fulfillment networks.

PhD student internship focused on applying machine learning, NLP, computer vision, automated reasoning, or robotics to solve complex business problems and deploy solutions into production at Amazon.

Applied Scientist II role focused on leveraging GenAI/LLMs to build agentic solutions for financial insights and actions within Amazon. The role involves developing AI trust and safety in the financial domain, creating training/evaluation datasets for fine-tuning, and collaborating with engineers for productionalization. It balances scientific research with production deployment, with opportunities for external publications.

Software Development Engineer focused on building and scaling real-time agentic AI systems for Alexa's communication features, involving design, architecture, and full-stack development.

Software Development Engineer role focused on building agentic APIs and integrating advanced AI technologies (Generative, Agentic, Real-time AI) into Alexa's communication features. The role involves influencing product strategy, optimizing for low latency and scalability, and ensuring high-quality software delivery within an Agile environment.

Senior Software Development Engineer role focused on building agentic APIs and cloud services for Alexa Connections, leveraging Generative, Agentic, and Real-time AI to enhance conversational experiences. The role involves influencing product strategy, optimizing software for performance, and leading integration of AI systems.

Software Development Engineer role focused on building and deploying computer vision and machine learning systems for Amazon's global fulfillment network. The role involves end-to-end ownership of systems that provide vision-based insights for item identification, defect detection, and decision-making in manual and robotic operations, supporting downstream business processes and flagship devices.

Software Development Engineer role focused on building agentic APIs and integrating advanced AI technologies (Generative, Agentic, Real-time AI) into Alexa's communication features. The role involves influencing product strategy, optimizing for low latency and scalability, and ensuring high-quality software delivery within an Agile environment.

Software Development Engineer role focused on building agentic APIs and integrating advanced AI systems (Generative, Agentic, Real-time AI) into Alexa's communication features, aiming for low latency, high reliability, and scalability.

Software Development Engineer II on the AWS Elemental Inference team, focused on building and shipping production code for AI-driven video processing systems. The role involves translating research into scalable services, improving performance, and implementing MLOps best practices for AI-enhanced systems.

This role focuses on developing and deploying machine learning models for communication experiences within Alexa. Responsibilities include end-to-end model development, experimentation, and leveraging LLMs to build applications, aiming to improve customer engagement and product performance.

This role focuses on building AI agents to optimize end-to-end workflows for Amazon's Advertising Sales organization. It involves applying expertise in NLP, LLMs, Deep Learning, Reinforcement Learning, and Recommender Systems to create and refine production-ready models, with a strong emphasis on autonomous agents operating at scale. The scientist will also collaborate with engineering and product teams, conduct A/B experiments, and contribute to scientific publications.

Applied Scientist role focused on building AI/ML solutions for Amazon's Advertising Sales business. The role involves developing and implementing models for insights, recommendations, and generative AI-powered workflows to improve sales team efficiency and customer success. It requires expertise in quantitative modeling, deep learning, RL, and NLP, with a focus on production deployment and A/B experimentation.

Applied Scientist role focused on building and refining Generative AI and ML models to optimize Amazon's Ad Sales business. The role involves conceptualizing research, guiding technical approaches, conducting data analysis, running A/B experiments, and working with engineers to deliver end-to-end solutions into production. The goal is to transform account team operations with actionable insights, recommendations, and GenAI integration for improved efficiency.

Software Development Engineer role focused on building and deploying ML and GenAI models to resolve disruptions in Amazon's Middle Mile Transportation network. The role involves developing intelligent systems for predictive analytics and automation, operating high-volume, low-latency services, and collaborating with stakeholders.

Software Development Engineer role focused on building intelligent systems powered by ML and GenAI to resolve transportation disruptions. The role involves building and deploying ML models for predictive analytics and leveraging generative AI for automation and decision support within Amazon's Middle Mile Transportation Technology team.

Applied Scientist role focused on Generative AI and quantitative modeling for Amazon Advertising Sales. The role involves conceptualizing and leading research on ML/GenAI solutions, guiding technical approaches, conducting data analysis, running A/B experiments, and working with engineers to deliver end-to-end solutions into production. Key areas include optimizing sales business, improving work efficiency through GenAI, and developing advertiser insights and recommendations.

The Sr. Software Development Manager will lead a team of compiler engineers developing, deploying, and scaling a compiler targeting AWS Inferentia and Trainium ML accelerators. This role involves deep knowledge of resource management, scheduling, code generation, and optimization for new instruction architectures, with a focus on delivering high-performance, low-cost ML inference and training solutions for AWS customers.

The role focuses on optimizing the performance of machine learning kernels for AWS's custom ML accelerators (Inferentia and Trainium) by developing and implementing high-performance compute kernels, optimizing compiler optimizations, and analyzing kernel-level performance. This involves working at the hardware-software boundary to ensure optimal performance for deep learning and GenAI workloads.

Data Scientist II at Amazon Private Brands focused on applying Generative AI, Machine Learning, Statistics, and Economics to product assortment, business decisions, and product inputs. The role involves investigating business problems, inventing novel solutions, prototyping, and deploying production software, with research areas including NER, product substitutes, pricing optimization, agentic AI, and LLMs. The Data Scientist will also guide other scientists and publish research.

Applied Scientist role focused on designing and building machine learning solutions for customer discovery of Amazon's Private Brands. The role involves end-to-end project management from ideation to launch, with a strong emphasis on causal ML, deep learning, and deploying models to production. The goal is to drive customer awareness and product discovery, impacting Amazon's own brands and contributing to broader discovery solutions across the company.

The AWS Neuron team is seeking ML Compiler Engineers to optimize deep learning and GenAI workloads on AWS custom ML accelerators (Inferentia/Trainium). This role involves analyzing and optimizing system-level performance across the entire technology stack, from frameworks to runtime, and designing/implementing compiler optimizations. The position requires a passion for performance analysis, distributed systems, and machine learning, with a focus on improving the performance capabilities of the AWS Neuron SDK.

The Annapurna Labs team at Amazon is seeking a Senior ML Kernel Performance Engineer to optimize deep learning and GenAI workloads on Amazon's custom ML accelerators (Inferentia and Trainium). This role involves crafting high-performance kernels, pushing the boundaries of AI acceleration at the hardware-software boundary, and collaborating with customers to enable their models. The engineer will work on compiler optimizations, performance analysis, and contribute to future architecture designs.

Manager III leading a team of compiler engineers to develop, deploy, and scale a compiler targeting AWS Inferentia and Trainium ML accelerators. The role involves technical leadership, innovation, and collaboration with AWS ML services teams to ensure the Neuron SDK meets customer needs for high performance, low cost, and ease of use. Deep knowledge of resource management, scheduling, code generation, and optimization is required.

The role focuses on optimizing the performance of machine learning kernels for AWS's custom ML accelerators (Inferentia and Trainium) by developing and implementing high-performance compute kernels, optimizing compiler optimizations, and analyzing kernel-level performance. This involves working at the hardware-software boundary to ensure optimal performance for deep learning and GenAI workloads.

Product Marketing Manager for Amazon Canada, focusing on seasonal events, merchandising, social media, and customer engagement. The role involves campaign execution, content optimization, data analysis, and reporting. While not directly building AI, the role explores and utilizes generative AI tools for workflow efficiency and identifying enhancement opportunities.

This role focuses on building and operating critical infrastructure services for Amazon's retail platform, dealing with massive traffic volumes in real-time using advanced machine learning systems and distributed infrastructure. The core responsibility involves improving operational excellence, performance, and monitoring of these services.