Amazon

Software Development Manager leading a team to build AI-powered customer support products for Amazon Advertising, focusing on AI agents and automation for advertiser case resolution and campaign management.

Software Development Engineer role focused on building AI/ML-powered products and infrastructure for data integration workflows within Amazon's AWS Utility Computing organization. The role involves designing, developing, and optimizing AI/ML systems, collaborating with data scientists, and contributing to the full software development lifecycle.

Software Development Manager for GenAI Evaluation Media (GEM) team at Amazon, focusing on visual shopping experiences. The role involves leading a team to drive technical vision and execution of AI capabilities for generating and governing billions of visual assets at scale, ensuring quality and trustworthiness for customer purchase decisions. Requires strong technical expertise, business acumen, and leadership skills to partner with stakeholders, drive innovation, and maintain operational excellence.

Software Development Engineer role focused on building solutions for Sponsored Products and Brands, utilizing generative AI, machine learning, and high-scale low-latency systems to improve customer experiences off the Amazon store. The role involves designing and developing components, big data systems, and ML-based approaches in collaboration with scientists and economists.

Software Development Engineer role focused on building and managing Agentic AI capabilities for AWS analytics services, specifically an Apache Spark Upgrade Agent that uses conversational interfaces and automated code transformation to reduce upgrade timelines. The role also involves managing a Model Context Protocol server for AI assistants and Agents, providing secure access to troubleshooting and code recommendation tools.

This Data Scientist II role focuses on building scientific tooling for how business customers interact with Long-Term Planning and Forecasting (LTPF) forecasts and plans. The role involves developing causal inference models, automated explainability frameworks, and variance bridging methodologies. It also includes building GenAI-powered narrative generation capabilities and automated hypothesis ranking to synthesize quantitative variance outputs into human-readable performance summaries and identify drivers of forecast error. The position emphasizes leading cross-functional programs, defining multi-year strategy, and leveraging insights for strategic decision-making.

This role focuses on developing causal inference models, automated explainability frameworks, and GenAI-powered narrative generation to translate forecasting outputs into actionable business intelligence for Amazon's business customers. The data scientist will build automated variance decomposition models and a causal model library with standardized pipelines, applying techniques from causal inference and time-series econometrics.

Applied Scientist II role focused on developing and deploying machine learning models and data-driven solutions for operational efficiency in Amazon's Fulfillment Centers and Transportation Sites. The role involves working with diverse datasets and ML frameworks to create predictive models, optimization algorithms, and intelligent automation, with a focus on scalability and production readiness. The team specifically handles network device management through a user-friendly, auditable mechanism for provisioning, configuration, and policy management.

Applied Scientist role focused on building causal ML systems for Amazon's pricing decisions. The role emphasizes shipping production-quality pipelines with real business impact, bridging econometric analysis and ML pipelines. Responsibilities include designing, training, and deploying causal estimation models, owning causal ML methodology, supporting pricing experiment analysis, and connecting model outputs to business outcomes.

This role focuses on implementing end-to-end AI/ML and GenAI solutions for enterprise customers using AWS services. The consultant will design, deploy, and manage these solutions, focusing on MLOps and customer success throughout the project lifecycle.

Senior Delivery Consultant for AWS Professional Services focused on designing, implementing, and managing AI/ML and Generative AI solutions for enterprise customers. This role involves leading project teams, hands-on development, MLOps, and advising customers on cloud AI/ML architectures using AWS services.

Software Development Engineer role focused on building AI-powered planning infrastructure for Amazon Ads. The role involves designing and delivering agentic skills, multi-step orchestration workflows, scalable APIs, and ML-informed optimization services. The tech stack includes TypeScript, Python, and AWS services.

Data Scientist II role focused on building LLM-powered financial forecasting and knowledge retrieval systems, including RAG, for Amazon's WW Ops FP&A team. Requires translating business ambiguity into production-grade AI solutions and influencing roadmaps.

This role is for a Senior Delivery Consultant focused on AI/ML solutions within AWS Professional Services. The consultant will design, implement, and manage AWS-based AI/ML applications and models for customers, providing technical guidance and support throughout the project lifecycle. Key responsibilities include gathering requirements, proposing model training and deployment strategies, and acting as a trusted advisor on AI/ML trends. The role requires experience with AWS AI/ML services and deploying LLMs in production.

Senior Systems Development Engineer role focused on developing AI/ML systems, autonomous robotics, and agentic workflow automation for facility design and deployment within Amazon's global fulfillment network. The role involves identifying and implementing technical solutions, developing internal tooling, contributing to product roadmaps, and mentoring engineers, with a strong emphasis on AI and innovation.

This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models. The focus is on architecting scalable, reliable, and secure solutions, supporting the adoption of AWS services, and providing feedback to product teams. The role also involves creating technical content and presenting at events.

Software Development Manager for Amazon Leo AI Foundations, responsible for designing, implementing, and operating globally distributed systems for a low Earth orbit satellite network. The role focuses on building near real-time analytics, a data lakehouse, and supporting agentic AI capabilities, including architecting a vector database with sub-100ms retrieval times and hybrid search strategies. The team builds the intelligent cloud backbone for AI-driven decision making across the Leo constellation, optimizing network performance and user experience.

Manager for a Data Science team focused on optimizing outbound communications for millions of customers using AI/ML, including LLMs and RL. The role involves leading the development of personalized messaging strategies, designing experiments, building propensity models, driving AI transformation in data accuracy and reporting, and owning financial planning frameworks. It also includes hiring, mentoring, and cross-functional partnership.

Software Development Engineer role focused on integrating Large Language Models (LLMs) into Alexa's audio experiences, aiming to improve model accuracy and reduce latency. The role involves designing and delivering software, creating infrastructure for LLMs, developing tools for evaluation, and influencing the operational roadmap for core audio services.

Software Development Engineer on the Sponsored Products and Brands Off-Search team, focused on building hyper-scale systems and data pipelines leveraging Generative AI and LLMs to improve advertising and shopping experiences. The role involves designing and delivering scalable solutions for ad retrieval, allocation, and recommendations, transforming these into personalized and contextually aware parts of the customer journey.

This role focuses on building AI-powered analytics experiences, specifically conversational agents and prompt-driven interfaces using LLMs and generative AI, to provide proactive insights for Amazon's advertising supported publishers. The engineer will own the end-to-end insight lifecycle, from data modeling to executive recommendations, and mentor other engineers.

Software Development Engineer III on the Amazon Private Brands (APB) team, responsible for building and evolving the core platform capabilities for a shared data, workflow, and AI agent platform. This includes dynamic data management, task orchestration, and AI agent infrastructure built on Amazon Bedrock. The role involves full-stack development, influencing technical direction, and partnering with consuming teams to ensure platform scalability. The engineer will own and operate backend services, lead technical roadmap, drive architecture decisions, mentor junior engineers, and translate business requirements into technical solutions.

Applied Scientist II role focused on developing and implementing machine learning and causal inference models for Amazon's advertising incrementality measurement. The role involves end-to-end model development, improving existing solutions, and collaborating with product and engineering teams to bring solutions to production. The team builds scientifically-rigorous causal inference methodologies at scale to help customers understand the impact of advertising.

Applied Scientist role at Amazon Ads focused on developing and productionizing end-to-end measurement solutions using Generative AI, classical ML, Causal Inference, NLP, and Computer Vision to provide insights and automate workflows for advertisers.

Senior Applied Scientist role focused on developing and productionizing AI/ML models for ad measurement and performance analysis within Amazon Ads. The role involves end-to-end solution development, leveraging Generative AI, classical ML, NLP, and Computer Vision to provide insights and automate workflows for advertisers. Responsibilities include model development, experimentation, collaboration with engineering and business teams, and mentoring junior scientists.

Robotics Engineer role focused on the design, deployment, and management of advanced robotic systems at scale within AWS data centers. The role involves developing robust robotic solutions, deploying them across multiple locations, and ensuring reliability, efficiency, and scalability. It requires collaboration with external partners and enterprise customers, and experience with vision language actuation models and shipping production code.

Software Development Engineer II role focused on building next-generation AI-powered pricing systems for Amazon's digital products. The role involves processing millions of prices daily using advanced AI models like LLMs and GenAI, working with AWS infrastructure, and designing distributed systems at scale.

Software Development Engineer II role focused on building next-generation AI-powered pricing systems for Amazon's digital products. The role involves processing millions of prices daily using advanced AI models like LLMs and GenAI, working with AWS infrastructure, and designing distributed systems at scale.

Software Development Engineer II role focused on building next-generation AI-powered pricing systems for Amazon's digital products. The role involves processing millions of prices daily using advanced AI models like LLMs and GenAI, working with AWS infrastructure, and designing distributed systems at scale.

Data Scientist role focused on leveraging advanced analytics and machine learning to protect AWS cloud infrastructure from security threats. The role involves developing and deploying anomaly detection models, predictive algorithms, and real-time analysis systems, as well as building and expanding LLM agent pipelines. Key responsibilities include analyzing large-scale security data, creating automated systems for threat identification, building data pipelines, and translating analytical findings into actionable insights. Experience with Python, statistical analysis, and machine learning is essential, with a focus on using AWS technologies like SageMaker and EMR.

Software Development Engineer role focused on integrating Large Language Models (LLMs) into Alexa's audio experiences, aiming to improve model accuracy and reduce latency. The role involves designing and delivering software, creating infrastructure for LLMs, developing tools for evaluation, and influencing the operational roadmap for core audio services.

Software Development Engineer II role focused on building next-generation AI-powered pricing systems for Amazon's digital products. The role involves processing millions of prices daily using advanced AI models like LLMs and GenAI, working with AWS infrastructure, and designing distributed systems at scale.

Software Development Engineer role focused on building and optimizing AI/ML-driven optimization services and recommender systems for Amazon Sponsored Products, aiming to improve advertiser campaign performance and product discovery. The role involves designing scalable APIs, deploying ML algorithms, and optimizing models for high-throughput, low-latency production environments.

Senior Software Development Engineer on the Gravemind team within Amazon Security, focusing on leading the design and implementation of AI-powered security automation, including intelligent incident response and natural language-driven security workflows. The role involves owning architecture, mentoring engineers, and driving operational excellence for services supporting 24/7 security operations. It also includes building scalable data pipelines and analytics solutions.

Senior Applied Scientist role focused on developing and productionizing AI/ML models for ad measurement and performance analysis within Amazon Ads. The role involves end-to-end solution development, leveraging Generative AI, classical ML, NLP, and Computer Vision to provide insights and automate workflows for advertisers. Responsibilities include model development, experimentation, collaboration with engineering and business teams, and mentoring junior scientists.

Applied Scientist role focused on building production ML infrastructure for customer analytics, including offline training pipelines, online scoring systems, and monitoring. The role involves signal analysis, pattern discovery, and predictive modeling, with a focus on behavioral signals from AI agents and agentic workflows, aiming to ship AI-augmented customer experiences.

Senior Applied Scientist role focused on building and deploying ML models for customer analytics and segmentation within AWS. The role involves contributing to customer understanding through segmentation models, behavioral classifiers, and predictive frameworks, with a strong emphasis on taking models from research to production. It requires building production ML infrastructure, framing new modeling problems, and collaborating with engineering teams to integrate ML systems into the CLARA platform and other frameworks. The role also involves contributing to scientific direction, mentoring, and documentation, with a focus on shipping models at scale and adapting to evolving customer behaviors driven by AI and agentic workflows.

Software Development Engineer II for the Conversational Ad Experiences (CAX) team within Amazon Ads, focusing on sponsored conversational shopping experiences powered by large language models (LLMs). The role involves designing and building net new conversational ad experiences on the Search page, integrating AI-driven solutions, and creating scalable systems for personalized ad experiences. This position requires building large-scale, low-latency distributed systems, ML infrastructure, data processing pipelines, and contributing to LLMOps, with a focus on customer-facing functionality impacting millions of shoppers.

Software Development Engineer to design, build, and operate production systems for Alexa's AI runtime, focusing on agent communication platforms, intelligent request routing, and unified agent ingress. The role involves building infrastructure for AI agents and transforming LLM outputs into real-time responses.

Software Development Engineer II on Amazon's Devices & Services Trust CX Innovations team, focusing on building responsible AI for consumer devices like Alexa and Echo. The role involves architecting hybrid AI systems (on-device vs. cloud), implementing privacy techniques (federated learning, differential privacy), developing AI evaluation frameworks, and building observability for AI performance and trust metrics. Key challenges include optimizing latency vs. privacy, reducing hallucinations, and ensuring privacy in ambient computing environments, with a focus on multimodal AI systems.

Software Development Engineer II on Amazon's Devices & Services Trust CX Innovations team, focusing on building responsible AI for consumer devices like Alexa and Echo. The role involves architecting hybrid AI systems (on-device vs. cloud), implementing privacy techniques (federated learning, differential privacy), developing AI evaluation frameworks, and building observability for AI performance and trust metrics. Key challenges include optimizing latency vs. privacy, reducing hallucinations, and ensuring privacy in ambient computing environments, with a focus on multimodal AI systems.

Software Development Engineer focused on AI for Prime Video's personalization and discovery, developing and guiding machine learning models from scratch. The role involves using Deep learning, GenAI, and Reinforcement Learning for search systems, leading end-to-end AI solution delivery, and conducting A/B experiments. While research publication is encouraged, the primary focus is on engineering and delivering AI solutions for customer-facing products.

Software Development Engineer focused on AI for Prime Video's personalization and discovery, developing and guiding machine learning models from scratch. The role involves using Deep learning, GenAI, and Reinforcement Learning for search systems, leading end-to-end AI solution delivery, and conducting A/B experiments. While research publication is encouraged, the primary focus is on engineering and delivering AI solutions for customer-facing products.

Solutions Architect focused on designing and implementing AI/ML and agentic AI solutions for National Security customers, leveraging AWS services. The role involves customer engagement, technical thought leadership, architecture design, and influencing product development for classified environments.

This role leads a multi-disciplinary team of applied scientists and engineers focused on developing and deploying generative AI technologies for Amazon's Sponsored Products and Brands. The team works on complex engineering, optimization, and user-experience problems to deliver relevant product ads, driving both shopper experience and business monetization. Responsibilities include leading ML projects, building and deploying models, running experiments, establishing scalable processes, and researching new ML approaches.

This role involves working with early-stage startups to help them leverage AWS technology for developing, training, tuning, and deploying generative AI foundation models. The focus is on providing architectural guidance, supporting the adoption of AWS services, and acting as a technical advisor to help startups build scalable, reliable, and secure solutions.

Senior Software Engineer role focused on building and optimizing the AWS Neuron compiler for AWS Inferentia and Trainium chips. The role involves transforming ML models (PyTorch, TensorFlow, JAX) into optimized implementations for deep learning workloads, with a focus on large language models and vision transformers. Responsibilities include compiler optimization, working with chip architects, and contributing to open-source communities.

Senior Software Development Engineer for the Conversational Ad Experiences (CAX) team within Sponsored Products and Brands (SPB). The role involves driving major product and engineering initiatives, building large-scale, low-latency distributed systems, and developing ML infrastructure and data processing pipelines. The engineer will partner with scientists to implement LLM models in production and contribute to LLMOps, impacting customer-facing functionality for millions of shoppers.