Senior Applied Scientist role focused on developing and implementing novel deep learning foundation models, combining multiple modalities (image, video, geospatial) for logistics use cases. The role involves training models at scale, optimizing for inference, collaborating with other teams, guiding technical direction, and mentoring junior scientists. It spans the full spectrum from data preparation to model training, evaluation, and inference.

Applied Scientist role focused on optimizing Generative AI models for edge devices, involving quantization, pruning, distillation, and fine-tuning. The role also requires understanding and inventing optimization techniques for custom ML hardware and collaborating with hardware architects and compiler engineers. The goal is to develop production-ready edge models and publish research findings.

Scientist role focused on designing and developing machine learning solutions for customer behavior analytics, utilizing deep learning, LLMs, recommendation systems, and reinforcement learning. Key responsibilities include fine-tuning generative models, developing recommendation and decision models, building behavioral representations, applying post-training optimization, and creating evaluation frameworks. The role emphasizes measurable business impact and customer satisfaction.

This role focuses on designing and developing machine learning solutions for customer behavior analytics at Amazon. Key responsibilities include fine-tuning language and generative models, developing recommendation and decision models, building temporal representations of customer behavior, and applying post-training optimization techniques. The role also involves developing evaluation frameworks and working with business and engineering teams to drive personalized customer experiences and business impact.

Machine Learning Engineer to join AWS Professional Services (ProServe) team. The role involves designing, implementing, and scaling AI/ML solutions for customers, applying Generative AI algorithms to solve real-world problems, and providing expertise throughout the project lifecycle. Responsibilities include architecting solutions, selecting and fine-tuning models, developing proof-of-concepts, running experiments, and providing technical guidance on responsible AI usage.

Applied Scientist II role at Amazon focusing on designing and developing scalable machine learning solutions for language translation services using GenAI/LLMs. The role involves applying expertise in LLM models, conducting data analysis, and collaborating with cross-functional teams to improve translation accuracy and efficiency for millions of customers worldwide.

Machine Learning Engineer to join AWS Professional Services (ProServe) team. The role involves designing, implementing, and scaling AI/ML solutions for customers, applying Generative AI algorithms to solve real-world problems, and providing expertise throughout the project lifecycle. Responsibilities include architecting solutions, selecting and fine-tuning models, developing proof-of-concepts, running experiments, and providing technical guidance on responsible AI usage.

Applied Scientist role focused on developing and customizing large language models for enterprise use cases, involving techniques like supervised fine-tuning, reinforcement learning, and knowledge distillation. The role requires building enterprise-ready tooling, optimizing models, and contributing to responsible AI toolkits.

Senior Applied Scientist role focused on developing and deploying AI/ML solutions for healthcare, specifically involving LLMs and VLMs, with an emphasis on model optimization and fine-tuning for production.

The Data Scientist-II role on the Alexa Sensitive Content Intelligence (ASCI) team focuses on building AI safety systems for Alexa's next-generation AI-powered virtual assistant. This involves developing responsible AI (RAI) solutions to ensure LLMs provide safe and trustworthy responses, understanding nuanced human values, and maintaining customer trust. The role requires applying state-of-the-art Generative AI techniques to analyze data, run experiments, and optimize data for sensitive content detection and mitigation, working with LLMs and multimodal systems.

The Data Scientist-II role on the Alexa Sensitive Content Intelligence (ASCI) team focuses on building AI safety systems for Alexa's next-generation AI-powered virtual assistant. This involves developing responsible AI (RAI) solutions to ensure LLMs provide safe and trustworthy responses, understanding nuanced human values, and maintaining customer trust. The role requires applying state-of-the-art Generative AI techniques to analyze data, run experiments, and optimize data for sensitive content detection and mitigation, working with LLMs and multimodal systems.

Applied Scientist II role focused on developing and optimizing state-of-the-art AI/ML solutions for healthcare, specifically LLMs and VLMs, with a focus on production deployment and model distillation.

Senior Software Development Engineer focused on building and scaling foundational LLMs for Amazon Stores. The role involves architecting and building ML infrastructure for LLM training and post-training workflows (fine-tuning, RL, continuous learning), transforming customer interactions into training signals, optimizing RL systems, and partnering with scientists to productionize frontier techniques like RLHF and agentic workflows. Emphasis on end-to-end system ownership, including design, implementation, deployment, and observability, with a focus on low-level optimization like CUDA kernels and ML platforms.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.