Applied Scientist role at Amazon focusing on improving shopping experiences using LLMs. The role involves post-training of LLMs, including instruction tuning, reward modeling, and reinforcement learning. Responsibilities include designing and running large-scale experiments, analyzing model behavior, and developing new training recipes to enhance capabilities like reasoning and user experience. Requires a PhD or Master's with significant experience, practical LLM experience, and a strong publication record.

Develop computer vision models on overhead imagery for a government customer, owning the entire ML development lifecycle from data exploration and feature engineering to model training, evaluation, and delivery. This role operates on classified networks and requires a Top Secret security clearance.

Senior Machine Learning Engineer role focused on designing, implementing, and scaling AI/ML solutions for AWS customers. The role involves working with customers to understand their needs, select and fine-tune models, develop proof-of-concepts, and implement AI/ML solutions at scale. It also includes designing and running experiments, researching new algorithms, and optimizing for business impact. The role requires expertise in machine learning, generative AI, and best practices, with a focus on customer success and AI transformation.

The Applied Scientist will work on building industry-leading Conversational AI Systems, focusing on Natural Language Understanding, Dialog Systems, Generative AI with LLMs, and Applied Machine Learning. The role involves developing novel algorithms and modeling techniques to advance human language technology, impacting millions of customers through products and services. The scientist will gain hands-on experience with Amazon's text, structured data, and large-scale computing resources.

Applied Scientist II role focused on developing foundation models for industrial robotics, integrating multi-modal learning, skill acquisition, perception, and environmental understanding. The role involves leveraging, adapting, and optimizing state-of-the-art models, conducting rigorous experimentation, building evaluation benchmarks, and contributing to data and training workflows. It requires strong programming skills in Python and experience in deep learning areas like computer vision, multimodal models, or RL for robotics.

ML Engineer role focused on developing customization capabilities like fine-tuning and distillation for LLMs, advancing LLM training techniques, and optimizing multimodal LLMs and Generative AI solutions. Requires experience deploying LLMs in production and knowledge of ML frameworks.

Machine Learning Engineer II on the AGI Customization team at Amazon, focusing on developing and optimizing LLM training techniques, including fine-tuning, distillation, model evaluation, and prompt optimization for multimodal LLMs and Generative AI solutions.

Senior Applied Scientist role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a strong emphasis on research, publication, and deploying these models into production systems for developers.

This role focuses on optimizing and fine-tuning Generative AI models for edge platforms, working closely with custom ML hardware. The scientist will train custom models, analyze deep learning workloads, and collaborate with cross-functional teams to build ML-centric solutions for consumer devices. The role also involves publishing research and presenting at ML conferences.

Senior Software Engineer focused on performance optimization for distributed AI model training on AWS Trainium accelerators. The role involves working with frameworks like PyTorch and JAX, optimizing the Neuron software stack, and improving training throughput and efficiency for large-scale models.

The Principal Applied Scientist role at Amazon's Alexa International team focuses on defining research directions, inventing and applying ML techniques, conducting experiments, publishing results, and translating research into practice for expanding Alexa's reach across countries, languages, devices, and cultures. The role requires a PhD in AI/ML/NLP with 10+ years of experience, a strong publication record, and expertise in building and deploying ML solutions at scale.

Data Scientist II role focused on applying statistical, machine learning, and GenAI methodologies to enhance employee experience within Amazon's People Experience and Technology organization. The role involves designing, developing, and maintaining scalable models and prototypes, partnering with cross-functional teams, and creating benchmarks for GenAI model performance.

This role focuses on analyzing and evaluating conversational interaction data to support the training and evaluation of LLMs and machine learning models for Alexa's speech interfaces. The Language Data Scientist will own data analysis, research requests, and contribute to developing annotation workflows and evaluation conventions.

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.

This role focuses on building and evaluating state-of-the-art ML models for biology and life sciences applications, requiring experience with deep learning methods and programming in languages like Python. The role is part of a special projects team aiming to innovate at scale and bring products to market.

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.

Economist II role focused on causal inference and machine learning for Prime Video Ads, involving experiment design, model building, and translating findings into business decisions. Requires a quantitative approach and end-to-end model implementation.

This role focuses on improving existing machine learning methodologies within Amazon's supply chain optimization technologies. Responsibilities include analyzing large datasets, developing new data sources, enhancing and testing models, running computational experiments, and fine-tuning model parameters. The role also involves formalizing model assumptions, identifying outliers, and communicating findings to various stakeholders. Collaboration with internal and external researchers, including publishing papers, is expected.

Data Scientist role focused on developing and implementing statistical, causal, and machine learning techniques for forecasting and inventory management within Amazon's retail supply chain. The role involves creating prototypes, collaborating with software teams for production implementation, and analyzing key business metrics to influence business direction.

Senior Computational Biologist role focused on developing advanced computational methods and predictive models for complex, multi-modal datasets in the healthcare space. The role involves building interpretable models, integrating diverse data sources, and uncovering actionable insights, operating within an entrepreneurial and rapidly evolving environment.

The Data Scientist II role at Amazon Prime Video focuses on analyzing customer viewing data to provide business insights and optimize content selection. The role involves developing and deploying new ML models using various data types to understand and predict customer behavior, supporting business reporting, and translating insights into actionable recommendations. The position requires strong data science, ML, and statistical skills, with experience in SQL, Python, and ML modeling techniques. The candidate will work with large datasets and collaborate with research scientists and economists to improve optimization across tools.

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.

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.

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.

Applied Scientist role at Amazon Prime Video focusing on content localization, understanding, and enrichment using NLP and computer vision. The role involves leading research direction, developing deep learning algorithms, and potentially building agentic systems for content understanding.

Applied Scientist III role at Amazon Web Services focusing on the design, development, evaluation, and deployment of data-driven models and analytical solutions for ML and NL applications. Responsibilities include applying statistical modeling, optimization, and ML techniques, building and deploying models in production, and researching novel ML approaches. Requires a Master's degree in a related field and experience in programming and developing supervised/unsupervised ML models.

Manager for a team working on LLM and/or VLM post-training and alignment for new personalized shopping experiences, leveraging customer behavioral data.

Applied Scientist role at Amazon Prime Video focusing on content reasoning, enrichment, and localization. The role involves research and application of machine learning, audio processing, and natural language understanding, with specific mention of multimodal machine translation, speech synthesis, speech analysis, and asset quality assessment. Requires experience in building models for business applications and a PhD or Master's degree. Familiarity with foundational models and speech synthesis is a plus.

This role focuses on curating and evaluating content to train and optimize AI models for conversational shopping experiences in new marketplaces and languages. It involves defining guidelines, ensuring response quality, analyzing errors, and creating frameworks for prompt tuning and management. The role also guides the development of automation and internal tools for editorial curation and evaluation, collaborating with product, science, and engineering teams.

Applied Scientist role focused on building and evaluating ML models for life sciences applications, specifically protein biology. Requires a PhD in a related field and expertise in ML/deep learning, with a preference for publication experience.

Applied Scientist role at Amazon Music focusing on building, training, and deploying ML models for customer experiences and business decisions. The role involves collaborating with scientists and engineers, experimenting with modern ML techniques, and implementing scalable data pipelines and model-serving systems. It's suitable for early-career individuals with a PhD or Master's degree and 3+ years of experience in building models for business applications.

Support Engineer role focused on implementing, fine-tuning, and troubleshooting AI-powered systems for compliance document validation using LLMs and ML algorithms. Requires Python, ML frameworks, AWS, and familiarity with compliance processes.

Support Engineer role focused on implementing, fine-tuning, and troubleshooting AI-powered systems for compliance document validation using LLMs and ML algorithms. Requires Python, ML frameworks, AWS, and familiarity with compliance processes.

Research scientist role focused on developing new AI technologies for personalization, specifically in recommendations, information retrieval, and fine-tuning LLMs, for Amazon's e-commerce platform. The role involves research, design, development, and launching new features and systems, with a strong emphasis on innovation and customer impact.

Software Development Engineer role focused on building and delivering consumer-facing conversational assistant features using advanced LLM techniques like fine-tuning and prompt optimization for Alexa AI.

MS or PhD student internship focused on research in machine learning, deep learning, generative AI, LLMs, speech, robotics, computer vision, optimization, OR, quantum computing, automated reasoning, or formal methods. The role involves designing and developing end-to-end systems, writing technical papers, creating roadmaps, and driving production-level projects. Experience with publications at top-tier conferences and solving business problems with ML/data mining/statistical algorithms is preferred.

Applied Scientist II role focused on designing and developing LLM-based machine learning solutions for language translation services at Amazon. The role involves applying expertise in LLMs, conducting data analysis, and evaluating new modeling techniques to improve translation accuracy and efficiency for millions of customers across 130+ locales. The team is leveraging Gen AI to build scalable solutions from scratch.

MS or PhD student internship in machine learning, deep learning, generative AI, LLMs, speech, robotics, vision, optimization, OR, quantum computing, automated reasoning, or formal methods. Focus on designing and developing end-to-end systems, writing technical white papers, creating roadmaps, and driving production-level projects. Opportunity to design new algorithms and models, deploy solutions into production, and potentially publish work.

Sr. Applied Scientist role focused on developing AI prototypes and concepts for the JP Manga business, involving research, design, and training/tuning of NLP and Computer Vision models for applications like translation, summarization, extraction, boundary detection, image understanding, and generation. The role emphasizes tangible business impact and collaboration with product managers and engineers, with opportunities for publication.

Data Scientist role focused on developing and implementing statistical, causal, and machine learning techniques for forecasting and inventory management within Amazon's retail supply chain. The role involves creating prototypes, collaborating with software teams for production implementation, and analyzing key business metrics to influence business direction.