Amazon

Senior Applied Scientist role focused on designing, implementing, and scaling decision-making algorithms for AWS EC2 capacity management. The role involves mathematical optimization, large-scale problem solving, and applying ML/Gen AI methods to enhance optimization algorithms. Responsibilities include data analysis, prescriptive optimization modeling, simulation, A/B testing, and collaborating with engineering and product teams.

Senior Business Intelligence Engineer focused on building and scaling personalized recommendations for millions of customers in EU marketplaces. The role involves developing custom models (embeddings, persona matching) using Python and Spark, building production-grade Python services, serverless infrastructure (AWS Lambda, CDK), and APIs (FastAPI) for marketing teams. It also includes owning reporting, measurement, and deep-dive analytics for personalized marketing campaigns.

This role focuses on prompt engineering for Generative AI and LLMs, involving the full prompt lifecycle from concept to production. Responsibilities include developing Python code, managing CI/CD pipelines, architecting contextual frameworks, and testing prompts to enhance AI performance and address real-world challenges like counterfeit detection within the Trustworthy Shopping Experience team.

This role focuses on ensuring the quality and reliability of AI model evaluations for Amazon's Seller Assistant copilot. The primary responsibilities involve benchmarking AI models, evaluating audits performed by a core auditing team, improving audit consistency, and enforcing quality standards. The goal is to scale AI model evaluation coverage and ensure high-quality outcomes for sellers.

This role focuses on ensuring the quality and reliability of AI model evaluations for Amazon's Seller Assistant copilot. The primary responsibilities involve benchmarking AI models, evaluating audits performed by a core auditing team, improving audit consistency, and enforcing quality standards. The goal is to scale AI model evaluation coverage and ensure high-quality outcomes for sellers.

This role focuses on ensuring the quality and reliability of AI model evaluations for Amazon's Seller Assistant copilot. The primary responsibilities involve benchmarking AI models, evaluating audits performed by a core auditing team, improving audit consistency, and enforcing quality standards. The goal is to scale AI model evaluation coverage and ensure high-quality outcomes for sellers.

This role focuses on prompt engineering for Generative AI and LLMs, involving the full prompt lifecycle from concept to production. Responsibilities include developing Python code, managing CI/CD, architecting contextual frameworks, and testing prompts to enhance AI performance and address real-world challenges, specifically within the Trustworthy Shopping Experience team at Amazon.

Senior Applied Scientist role focused on developing evaluation frameworks and data collection protocols for robotic capabilities. The role involves designing how to measure, stress-test, and improve robot behavior, building infrastructure for teleoperation, evaluation, and learning, and analyzing results to identify performance gaps. It requires expertise in robotics, ML, and human-in-the-loop systems, with a focus on turning capability goals into measurable evaluation systems.

This role focuses on designing and implementing intelligent automation solutions using AI, ML, LLMs, and RPA tools within Amazon's Accounts Receivable team. The engineer will optimize existing automations, partner with stakeholders, lead projects, and ensure reliability and integration across financial systems. Basic qualifications include RPA certifications, programming experience (Python/C#), and experience with RPA tools and APIs. Preferred qualifications involve deploying AI products at scale, data mining, and integrating LLMs with enterprise data using embeddings and vector search.

Software Development Engineer role focused on building conversational ad experiences using LLMs within Amazon's advertising business. The role involves driving product and engineering initiatives, building large-scale, low-latency distributed systems, ML infrastructure, data pipelines, and implementing LLM models in production, including LLMOps.

Software Development Engineer II role focused on architecting and delivering AI-powered design automation solutions for AWS's global data center infrastructure, integrating BIM platforms and AWS services. The role involves productionizing ML models, implementing human-in-the-loop workflows, and collaborating with applied scientists and domain experts.

Software Development Engineer II role focused on building generative AI applications for data center design automation within AWS Infrastructure Services. The role involves architecting and implementing scalable production systems that integrate BIM platforms, cloud services, and AI technologies, with a focus on human-in-the-loop workflows. The engineer will collaborate with applied scientists to productionize ML models and mentor junior engineers.

Senior Applied Scientist role focused on developing and improving sensing systems for advanced robotics at Amazon scale. This involves designing custom sensor systems, integrating AI and control systems, and working from concept to production in a fast-paced, interdisciplinary environment. The role requires deep knowledge in sensing, signal processing, computer vision, and machine learning, with a focus on shipping robotic products.

Senior Software Development Engineer role within AWS Commercial Application Services focusing on building cloud infrastructure for enterprise applications, specifically SAP workloads. The role involves leveraging Agentic AI and Generative AI to enhance operational efficiency, redefine cloud computing, and create new capabilities for customers. It requires designing and scaling mission-critical services, influencing product strategy, defining system architecture, and ensuring customer needs are met through AI-driven solutions. The position emphasizes building large-scale distributed systems integrated with AI capabilities.

This role focuses on integrating computer vision, edge computing, and physical automation systems to enable real-time operational intelligence, improve equipment performance, and optimize process flow within global fulfillment networks. The engineer will bridge AI/ML models with physical systems, leading the development and deployment of sensor-driven automation solutions and ensuring seamless integration across hardware, software, and control layers.

Senior Software Engineer role focused on building and enhancing AWS Bedrock AgentCore services within the Agentic AI field. The role requires developing scalable, efficient, and fault-tolerant machine learning powered solutions, with a preference for experience in ML systems. Responsibilities include delivering strategic technical projects, designing new software systems, mentoring engineers, and providing technical leadership in a fast-moving, startup-like environment within a large company.

Senior Software Engineer role at AWS EC2 focusing on building enterprise cloud infrastructure services that leverage Agentic and Generative AI for SAP workloads. The role involves designing, scaling, and integrating AI-driven innovations to enhance operational efficiency and customer experience, with a strong emphasis on autonomous systems and intelligent automation.

Software Development Engineer role focused on building a new AWS service for agentic AI, specifically enabling memory capabilities for autonomous agents. The role involves designing, developing, and deploying large-scale, fault-tolerant ML systems, collaborating with applied scientists, and mentoring junior engineers.

Senior Applied Scientist role focused on developing and deploying large-scale machine learning systems for fraud and risk prevention in an e-commerce environment. The role involves leading scientific strategy, designing and deploying ML systems, and leveraging Generative AI and LLMs to build next-generation platforms. It requires experience in building ML models, deep learning, and programming languages.

This role focuses on building and deploying advanced machine learning models for Amazon's consumer businesses in India and other emerging markets. The scientist will analyze large datasets, develop and evaluate scalable ML solutions, and work closely with engineering teams to implement these models in production. The role involves end-to-end ownership of business problems and directly impacting profitability.

This role focuses on ensuring the quality and reliability of AI model evaluations for Amazon's Seller Assistant copilot. The primary responsibilities involve benchmarking AI models, evaluating audits performed by a core auditing team, improving audit consistency, and enforcing quality standards. The goal is to scale AI model evaluation coverage and ensure high-quality outcomes for sellers.

This role focuses on ensuring the quality and reliability of AI model evaluations for Amazon's Seller Assistant copilot. The primary responsibilities involve benchmarking AI models, evaluating audits performed by a core auditing team, improving audit consistency, and enforcing quality standards. The goal is to scale AI model evaluation coverage and ensure high-quality outcomes for sellers.

This role focuses on ensuring the quality and reliability of AI model evaluations for Amazon's Seller Assistant copilot. The primary responsibilities involve benchmarking AI models, evaluating audits performed by a core auditing team, improving audit consistency, and enforcing quality standards. The goal is to scale AI model evaluation coverage and ensure high-quality outcomes for sellers.

Senior Applied Scientist role at Amazon Prime Video focused on developing and launching AI solutions for personalization and discovery, including search systems. The role involves end-to-end ownership of machine learning models, from design and implementation to experimentation and evaluation, with a focus on benefiting millions of customers. The position also requires staying updated with the latest modeling techniques and publishing research findings.

Senior Applied Scientist role focused on building predictive intelligence for capacity management in AWS workspaces. This involves developing ML systems for demand forecasting, resource optimization, and cost efficiency at enterprise scale. The role requires translating business needs into production ML systems, designing algorithms, and applying advanced ML techniques like time-series forecasting, reinforcement learning, and causal inference. Emphasis on low-latency, large-scale data processing, and collaboration with product and engineering teams.

ML Compiler Engineer II on the Neuron Compiler Automated Reasoning Group, developing and maintaining tooling for fuzzers and specification synthesis for an LLVM-based compiler targeting ML accelerators (Inferentia/Trainium) for domains like Large Language and Vision. Focus on accuracy and reliability of the compiler stack.

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.

Sr. Applied Scientist role focused on developing and implementing AI/ML solutions to improve the Amazon Selling Partner experience. This involves building scalable models, analyzing data, and staying current with LLM, RL, and agent-based AI research to translate findings into practical applications. The role requires strong ownership of the full project lifecycle and collaboration with business and engineering teams.

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 at Amazon Ads focusing on measurement science, developing solutions for performance measurement products, sales impact analysis, and AI tools for customer insights and workflow automation. The role involves end-to-end ownership of measurement solutions, from design to production, leveraging Generative AI, classical ML, Causal Inference, NLP, and Computer Vision.

This role bridges generative AI research and visual storytelling for Prime Video, focusing on translating ML capabilities into production workflows and understanding creative needs. The Sr. Design Technologist will assess generative models, build proof-of-concept tools, and identify gaps between model output and production requirements.

Software Development Engineer role focused on building core functionality for AWS Internet Monitoring service, leveraging Generative AI techniques for network monitoring, traffic engineering, and autonomous systems. The role involves designing, developing, and deploying AI-integrated software systems, collaborating with cross-functional teams, and ensuring operational excellence.

The Sponsored Products and Brands team at Amazon Ads is seeking an Applied Scientist to leverage generative AI and machine learning to revolutionize advertising experiences. This role involves driving end-to-end ML projects, analyzing large datasets, building and deploying models into production, and establishing scalable processes for ML development and serving. The focus is on creating innovative ad tech and shopping experiences off the store, working with external and internal partners to connect systems, understand customers, and drive results at scale, with a GenAI-first approach.

Senior Applied Scientist role focused on developing and launching AI solutions for Amazon's Sponsored Products and Brands advertising business. This involves building recommendation systems leveraging generative models, designing and implementing end-to-end AI solutions, and defining a long-term science vision. The role requires experience in building ML models, deep learning, and working with large-scale distributed systems.

Senior Solutions Architect for AWS, focusing on Agentic AI and AI-assisted software development for a major European telecommunications group. The role involves owning the end-to-end technical relationship, designing scalable and secure architectures, and influencing strategic technology decisions at CxO/VP level, while navigating complex sovereignty and compliance requirements.

Senior Software Engineer role focused on driving technical strategy and architecture for robotic software systems, including perception, navigation, motor control, reasoning, and human-robot interaction. The role involves end-to-end delivery of complex systems, leading collaboration across research and ML teams, defining technical roadmaps, and developing production-level SDKs. The team is building robots for everyday life, aiming to make robotics development more accessible and intuitive.

Manager for an AWS Applied AI Solutions team focused on building foundational systems and consumer-facing features for trustworthy AI experiences at scale within the supply chain domain. The role involves leading engineering teams, driving technical strategy for privacy-preserving and responsible AI, and developing enterprise-grade AI applications.

Security Engineer focused on threat detection using ML and generative AI to build and improve detection capabilities at scale for Amazon's network.

This role focuses on evaluating AI systems, specifically LLMs, by designing and executing benchmarking and audit activities. The core responsibilities include assessing model quality, compliance, robustness, and fairness, as well as handling annotations for training and measuring AI models. The role also involves preparing audit reports and ensuring data quality.

Applied Science Manager to lead a team building and scaling a prompt generation pipeline for a conversational ad format powered by LLMs. The role involves developing prompt themes, quality frameworks, and driving coverage expansion, with a focus on personalization and experimentation to improve key metrics. Requires strong technical depth in NLP, LLMs, and information retrieval, along with management experience and the ability to influence product strategy.

This role focuses on developing and deploying machine learning models for Amazon's Sponsored Products and Brands Ads, aiming to improve customer experience and advertiser effectiveness. The scientist will conduct data analysis, build and optimize ML models, run A/B experiments, and collaborate with engineers to productionize solutions. They will also research new ML modeling techniques to enhance business outcomes.

The Applied Scientist will build next-generation conversational shopping experiences for Amazon Rufus, developing data-driven solutions and deploying models into production. This role involves collaboration with cross-functional teams and leveraging Amazon's data and AWS resources.

Applied Scientist role focused on building GenAI-powered data solutions, agentic systems for quality issue detection, and ranking/recommendation models for e-commerce. The role involves designing, developing, testing, and deploying scalable ML solutions, working with state-of-the-art models like LLMs and image-to-text.

This role focuses on implementing and building ML/LLM solutions for business needs, collaborating with scientists, writing code, and optimizing solutions. It involves product pilots and developing technical documentation.

This role focuses on implementing and scaling Generative AI solutions for AWS customers, acting as a consultant to design, train, fine-tune, and deploy bespoke AI applications. It involves working with customers to identify use cases, navigate technical challenges, and ensure responsible and cost-efficient AI implementation.

The role focuses on building ML systems with a causal modeling foundation for AWS sales optimization, designing seller incentive mechanisms, and developing intervention strategies. It involves developing rigorous causal measurement and modeling frameworks, designing programs and incentives, and building ML-based recommendation systems.

The AI Benchmarking Lead will focus on ensuring the quality and reliability of AI model evaluations for Amazon's Seller Assistant copilot. This role involves benchmarking AI models, evaluating audit processes, improving audit consistency, and enforcing quality standards to support the scaling of the product to a wider seller base.

This role focuses on designing, developing, and deploying full-stack applications and data-intensive systems for supply chain optimization and automation. It involves building scalable backend services, frontend applications, data pipelines, and implementing end-to-end machine learning solutions within the Amazon production environment to improve supply chain forecasting and automation.