Amazon

The AI Data Associate will be responsible for foundational labeling functions, including dialogue evaluation on speech, text, audio, video data, to support the development and deployment of generative AI and LLMs at Amazon. This role involves working with diverse data types, delivering high-quality labeled data, analyzing root causes of errors, and proposing solutions to enhance labeling quality.

The role involves foundational data labeling for generative AI and LLMs, including text, speech, audio, image, and video data. Responsibilities include maintaining confidentiality, delivering high-quality labeled data according to KPIs, using in-house tools, analyzing root causes of errors, and suggesting process improvements. The role requires strong attention to detail, ability to make sound judgments, and proficiency in German.

Manager for Aurora PostgreSQL team focused on performance engineering, leveraging AWS infrastructure and machine learning to optimize database performance and drive the roadmap for feature and performance improvements.

Software Development Engineer role focused on building and scaling data processing and machine learning infrastructure using open-source technologies like Ray and AWS services. The role involves designing, developing, and contributing to large-scale distributed systems and data platforms that support internal customers including ML specialists and research scientists.

The AI Data Associate role focuses on foundational labeling tasks for AI development, including dialogue evaluation across various data modalities (text, speech, audio, image, video). The role requires delivering high-quality labeled data using in-house tools and guidelines, analyzing root causes of errors, and proposing solutions to improve labeling quality. This position is crucial for the responsible development and deployment of large language models (LLMs) at Amazon.

The AI Data Associate will be responsible for foundational labeling functions, including dialogue evaluation on speech, text, audio, video data, to support the development and deployment of generative AI and LLMs. The role involves working with various data types, delivering high-quality labeled data using in-house tools, and analyzing root causes to improve labeling quality.

This role focuses on content strategy and development for Amazon's employee amplification program, with a significant emphasis on leveraging and refining generative AI tools for content creation. The specialist will own content creation, messaging, and quality, ensuring alignment with brand guidelines and driving hiring outcomes. Key responsibilities include managing the content library, developing AI content prompts and outputs, creating targeted campaign content, crafting executive messaging, coordinating PR content, developing self-serve templates, localizing content, and measuring performance. The role requires a blend of creative storytelling, operational discipline, and analytical rigor, using AI tools to build scalable content systems while maintaining authenticity.

The AI Data Associate will be responsible for foundational labeling functions for generative AI and LLMs, working with various data types including text, speech, audio, image, and video. This role involves delivering high-quality labeled data, analyzing error patterns, and proposing solutions to improve labeling quality and operational processes.

The role involves foundational labeling functions for AI, including dialogue evaluation on text, speech, audio, video, and image data. The associate will deliver high-quality labeled data using in-house tools and guidelines, analyze root causes of errors, and propose solutions to enhance labeling quality. The goal is to support the development and deployment of generative AI and LLMs.

This role focuses on owning the data science elements of various products, including data-based decision making, product performance optimization, and tracking. The Data Scientist III will work with product managers to drive product design and build planning, translate business problems into data requirements, and initiate the design, development, and implementation of scientific analysis projects. Responsibilities include owning analysis, modeling, system design, and development of data science solutions, as well as communicating results to the business and bridging uncertainty in problem definition and solution approaches. The role requires building statistical and machine learning models using large datasets and scripting languages like Python or Java.

Economist role focused on applying econometrics and ML to optimize advertising strategies, influence business decisions, and analyze experiments within Amazon's advertising business. Requires a PhD in economics and experience with large datasets and statistical software.

Data Scientist III role focused on owning data science elements of products, including data-based decision making, performance optimization, and tracking. Responsibilities include working with product managers, translating business problems into data requirements, initiating and owning analysis, modeling, system design, and development of data science solutions, and communicating results. Requires experience in building statistical and machine learning models, complex data analyses using scripting languages (Python, Java), and communication for requirements gathering and process development.

This role focuses on language data, primarily in the areas of text annotation and general data analysis deliverables for Alexa AI. The ML Data Linguist will handle unique data analysis requests, provide data quality expertise, and contribute to process improvements. Responsibilities include labeling, generating, and ensuring the quality of datasets, working with ML Data Linguists and scientists to resolve annotation guideline issues, and conducting qualitative error trend analysis.

The role involves foundational labeling functions for generative AI and LLMs, including dialogue evaluation on speech, text, audio, and video data. The candidate will work with various data types, deliver high-quality labeled data using in-house tools, and analyze root causes to improve labeling quality. The role requires strong analytical thinking, problem-solving, and attention to detail, with fluency in Dutch and English.

The AI Data Associate will be responsible for foundational labeling functions, including dialogue evaluation on speech, text, audio, video data, to support the development and deployment of generative AI and LLMs at Amazon. This role involves working with diverse data types, delivering high-quality labeled data, analyzing root causes of errors, and proposing solutions to enhance labeling quality.

This role focuses on auditing and optimizing machine learning data workflows, acting as a deep expert in 1-2 workflows and leading quality audit teams. It involves hands-on audit execution, analysis of data quality, and providing insights for workflow improvement.

This role focuses on designing, developing, and maintaining data infrastructure and ETL pipelines to support business intelligence initiatives, with a specific emphasis on implementing AI/LLM-powered solutions for task automation and statistical analysis for business decision-making. The role involves collaborating with cross-functional teams to build AI-ready foundational capabilities within the Global Selling organization.

This role is for an Associate II, ML Data Operations at Amazon Robotics, focusing on human-in-the-loop data annotation for training and validating machine learning models. It's a non-technical role that involves precise annotation of text, video, and image data, identifying operational friction, and ensuring data quality for LLM capabilities. The role supports the development of robotics and fulfillment technologies.

This role focuses on preparing training data for ML algorithms using cloud infrastructure, performing architecture feasibility analysis, and evaluating video picture quality using industry tools and standards. The primary deliverable is the prepared training data for ML models.

Senior Economist role focused on building innovative solutions using econometrics, machine learning, and experimentation for Amazon Retail's product assortment. The role involves developing econometric models and experiments to measure customer and financial impact, influencing business decisions, and collaborating with a multidisciplinary team.

This role builds the foundational data platform for robotics and ML development, focusing on data pipelines, storage, and transformation to support ML training and fleet monitoring.

The JP Economics and Decision Sciences team is a central science team that applies rigorous economic theory, causal inference methods, and machine learning to solve complex business challenges across the JP marketplace and beyond. We work closely with JP business leaders to drive change at Amazon, focusing on solving long-term, ambiguous problems while providing advisory support for short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel economic and econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with JP-, EU-, and US-based interdisciplinary teams.

Research Scientist II role focused on psychometric aspects of exam development and operations, with a requirement to use ML, NLP, and GenAI for research and automation. The role involves developing and applying statistical and psychometric modeling to ensure exam validity and reliability, and automating psychometric workflows using R or Python. Experience with large-scale assessment programs and complex test designs is required.

Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production.

The Catalog Specialist will support Machine Learning (ML) models and data science classification algorithm development by performing data annotation and labeling for ML model training and evaluation. This role collaborates with product, technology, and science teams to ensure high-quality annotated data and improve annotation processes.

This role focuses on developing and building new ML and statistical forecasting models for Amazon Stores' financials, aiming to improve financial decision-making and planning for senior leadership. It involves working with stakeholders, collaborating with various teams for production implementation, and adapting new modeling techniques.

This role supports Machine Learning (ML) models and classification algorithm development by performing data annotation and ensuring high-quality standards for annotated data. The specialist collaborates with product, technology, and science teams to optimize model performance and improve annotation processes.

This role supports ML model development by performing data annotation and assisting data scientists with classification algorithms. The focus is on ensuring high-quality annotated data to optimize model performance within the consumer domain.

The role involves performing quality audits on data submitted by Data Associates, validating automated quality measurement systems through manual reviews, and coaching Data Associates to maintain high standards of data quality for AI development and training. The primary focus is on data quality assurance for AI training.

This role focuses on auditing and ensuring the quality of machine learning data workflows. The Senior Quality Auditor will become an expert in specific workflows, lead teams of auditors, execute complex audits, and provide analysis and insights for optimization. The role requires hands-on audit execution and leadership experience in technical domains.

This role focuses on building and maintaining scalable data pipelines, designing and implementing ontology structures, and enabling query grounding on knowledge graph systems. It involves using ETL software, programming languages, and LLMs for data cleaning, manipulation, and mapping. The role also includes analyzing pipeline performance, creating documentation, and performing quality assurance and root cause analysis on issues.

Lead high scope technical projects in Intelligent Talent Acquisition (ITA) Data team, focusing on data strategy, design, and hands-on development of complex products, mentoring data engineers, and ensuring high-quality data provision. Partner with stakeholders to develop scalable interoperability solutions.

This role focuses on auditing and ensuring the quality of machine learning data workflows. The Senior Quality Auditor will become an expert in specific workflows, lead teams of auditors, execute complex audits, and provide analysis and insights for optimization. The role requires hands-on audit execution and leadership experience in technical domains.

This role focuses on auditing and ensuring the quality of machine learning data workflows. The Senior Quality Auditor will become an expert in specific workflows, lead audit teams, execute complex audits, and provide analysis and insights for optimization. It requires experience with ML data workflows and leadership.

The role involves data annotation and evaluation for AI development, focusing on Korean language data including text, voice, images, and videos. Responsibilities include generating high-quality human judgment data, monitoring harmful content from generative AI, ensuring data privacy and security, and contributing to quality control and improvement by identifying error patterns and proposing solutions. The role also includes translation of guidelines and supporting AI development with appropriate decision-making.

Data Scientist III role focused on owning data science elements for products, including data-based decision making, product performance optimization, and tracking. Responsibilities include translating business problems into data requirements, designing and implementing analysis projects, owning analysis, modeling, and system design, and communicating results. Requires experience building statistical and machine learning models, performing complex data analyses with scripting languages, and communicating with stakeholders.

Data Scientist III role focused on owning data science elements for products, including data-based decision making, product performance optimization, and tracking. Responsibilities include translating business problems into data requirements, designing and implementing scientific analysis projects, and developing data science solutions. Requires experience in building statistical and machine learning models, complex data analysis with scripting languages, and communication with stakeholders.

This role focuses on ensuring data quality and strategic oversight for human-in-the-loop and model-in-the-loop data pipelines that support LLM training. The consultant will provide domain expertise, mentorship, and collaborate with AI teams to optimize data collection and evaluation processes.

This role focuses on overseeing and optimizing human-in-the-loop and model-in-the-loop data pipelines for AI solutions, ensuring data quality and providing mentorship. It involves collaborating with AI teams on data collection for LLM training and evaluating AI systems.

Data Scientist on the Core Tech Private Brands Analytics team, responsible for building and improving forecasting and planning models for Amazon Private Brands. This involves end-to-end pipeline development, replacing manual processes with code-driven solutions, and evaluating model accuracy. The role partners with business, science, and tech stakeholders and contributes to the organization's AI framework and experimentation rigor.

Data Engineer II role focused on building and maintaining data pipelines, including GenAI enhanced pipelines, to support AI Agent platforms and various analytics use cases within Ring Agent Platforms at Amazon.

This role focuses on quality auditing for AI development and training data, involving manual review and validation of automated quality measurements, coaching Data Associates, and identifying root causes of issues to maintain high data quality standards.

This role focuses on auditing and optimizing complex machine learning data workflows. The Senior Quality Auditor will become an expert in specific workflows, lead teams of Quality Auditors, execute audits on complex scenarios, and generate insights for workflow improvement. The role requires hands-on audit execution and leadership experience.

This role focuses on auditing and optimizing complex machine learning data workflows. The Senior Quality Auditor will become an expert in specific workflows, lead teams of Quality Auditors, execute audits on complex scenarios, and generate insights for workflow improvement. The role requires hands-on audit execution and leadership experience.

The Language Engineer will focus on dataset construction, linguistic annotation, dialog/semantic schemas, and automatic processing of large datasets to advance natural language processing and machine learning. Responsibilities include designing data collection tasks, analyzing data for insights, building data analysis tools, and collaborating with scientists to evaluate language models.

This role focuses on overseeing and optimizing human-in-the-loop and model-in-the-loop data pipelines for AI solutions, ensuring data quality and providing mentorship. It involves collaborating with AI teams on data collection for LLM training and evaluating AI systems.

This role focuses on providing strategic oversight and mentorship for human-in-the-loop and model-in-the-loop data pipelines, ensuring data quality for LLM training and evaluations. The consultant will also mentor junior team members and collaborate with AI teams to optimize data collection and define benchmarks for GenAI model performance.

This role focuses on providing strategic oversight and mentorship for human-in-the-loop and model-in-the-loop data pipelines, ensuring data quality for LLM training and evaluations. The consultant will also guide junior team members and collaborate with AI teams on data collection approaches.