Amazon

Research role focused on developing foundational Generative AI (GenAI) technology using Large Language Models (LLMs) and multimodal systems, involving model training, dataset design, and pre/post-training optimization.

Research-focused Applied Scientist role at Amazon working on generative AI for Alexa, focusing on LLMs, multimodal models, AI safety, alignment, and responsible AI. The role involves developing innovative solutions, optimizing models, evaluating performance, and leading the development of production-ready AI solutions, with a strong emphasis on research publications and patents.

Applied Scientist II at Amazon Alexa International focusing on developing and applying LLMs and multimodal systems for multi-lingual applications. The role involves research, fine-tuning/post-training LLMs, building evaluation metrics, and driving scientific strategy from research to production, impacting global customers.

Applied Scientist role focused on developing novel algorithms and modeling techniques for LLMs and multimodal systems within Alexa Connections. Responsibilities include analyzing customer behavior, building evaluation metrics, fine-tuning/post-training LLMs, setting up experimentation frameworks, and contributing to end-to-end delivery from research to production, with potential for publications.

PhD internship focused on applied science in Gen AI and LLMs, involving fine-tuning models, developing novel algorithms for NER, recommendation systems, and question answering, and exploring generative AI applications.

PhD internship focused on research in Natural Language Processing (NLP), Natural Language Understanding (NLU), and Speech Technologies, including large language models (LLMs) and reinforcement learning with human feedback (RLHF). The role involves developing and implementing novel algorithms on production-scale data to advance the state-of-the-art.

This role focuses on building AI safety systems for conversational AI, specifically for Alexa. It involves pioneering solutions in Responsible AI, training models for safety standards, designing automated testing for vulnerabilities, creating intelligent evaluation systems, building models to understand human values, and crafting feedback mechanisms. A key aspect is building AI agents for real-time detection and fixing of production issues. The role emphasizes frontier research with real-world impact, focusing on training truthful and grounded models, building reward models for human values, and creating automated systems to discover and address issues. Collaboration with scientists, PMs, and engineers is expected to transform ideas into production systems. The role also involves leading certification processes, advancing optimization techniques, building human-like evaluation systems, and mentoring others.

Applied Scientist role focused on building and evaluating LLMs and multimodal systems for Alexa Connections, involving fine-tuning, post-training, and contributing to research and production delivery.

Senior Applied Scientist role focused on Generative AI, LLMs, and multimodal models for Alexa+, emphasizing AI trust, privacy, safety, and alignment. The role involves leading research, developing optimization techniques, pioneering responsible AI methods, and collaborating with product and engineering teams to deliver production-ready AI solutions.

Principal Applied Scientist to lead science behind Alexa+, Amazon's LLM-powered conversational assistant. Owns technical direction for LLM fine-tuning, alignment, agentic reasoning, and evaluation, impacting hundreds of millions of customers. Defines research directions, designs experiments, ensures translation to production systems, mentors scientists, and represents Amazon in the research community.

Applied Scientist II role focused on designing, developing, and deploying data-driven models for ML and NL applications, with a strong emphasis on generative AI, NLP, and large-scale model training and deployment. The role involves researching and implementing novel ML approaches, fine-tuning foundation models, developing custom algorithms for model optimization, and conducting applied research on generative AI architectures and training strategies. Mentoring junior scientists is also a key responsibility.

This role focuses on advancing the state of the art with LLMs and multimodal systems for Alexa's international products. The scientist will develop novel algorithms, build evaluation metrics, fine-tune/post-train LLMs using advanced techniques (SFT, DPO, RLHF, RLAIF), and contribute to industry-first research. The role involves end-to-end delivery from research to production, influencing cross-team scientific strategy, and mentoring junior scientists. Key areas include multi-lingual applications, text, speech, and vision domains, with a strong emphasis on LLM evaluation and post-training methodologies.

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 on large datasets, optimizing for inference at scale, and collaborating with science and engineering teams for production deployments. It requires guiding technical direction, mentoring, and maintaining individual contributions.

Applied Science role focused on developing foundational capabilities for useful AI agents, leveraging large vision language models (VLMs) with reinforcement learning (RL) and world modeling. Responsibilities include model training, dataset design, and pre- and post-training optimization in an applied research setting.

Research role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a goal of deploying these models into developer tools like Kiro IDE and Amazon Q Developer at Amazon scale. The role involves publishing research and transitioning breakthroughs into production systems.

Research role focused on advancing LLM code intelligence through reinforcement learning and post-training methodologies, with a goal of deploying these models into developer tools like Kiro IDE and Amazon Q Developer at Amazon scale. The role involves publishing research and transitioning breakthroughs into production systems.

Seeking an Applied Scientist to develop industry-leading technology with LLMs and multimodal systems, focusing on advanced approaches, model-in-the-loop and human-in-the-loop for high-quality data collection and LLM training, and enhancing customer experiences.

Computer Vision Scientist role focused on developing and evaluating generative AI models for e-commerce media content, leveraging large datasets and cloud resources. The role involves research, implementation of novel ML techniques, and communication with stakeholders.

Principal Applied Scientist role focused on computer vision and multimodal LLMs, involving research, algorithm development, and translating research into practice for consumer products. Requires PhD, 10+ years of ML experience, and expertise in computer vision, VLM, and deep learning. The role involves defining research directions, developing long-term strategies, and mentoring junior scientists.

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 II role focused on developing and evaluating LLMs and multimodal systems for Alexa's international products. Responsibilities include analyzing customer behavior, building evaluation metrics, fine-tuning/post-training LLMs (SFT, DPO, RLHF, RLAIF), setting up experimentation, and contributing to research and production delivery. Requires strong ML, NLU, LLM architecture, and evaluation knowledge, with a focus on international customer nuances and diverse data sources.

Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery from research to production, impacting international customers with digital assistant technology.

Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery from research to production, impacting international customers with digital assistant technology.

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.

Research scientist role focused on developing neuro-symbolic AI systems that integrate proof assistants for enhanced learning and reasoning, applied across various Amazon domains. The role involves defining and implementing new applications, delivering scientific artifacts, and working in an agile environment.

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.

Applied Scientist role focused on researching and developing NLP, multi-modal, and LLM-based ML solutions for product compliance and safety within Amazon. The role involves evaluating existing algorithms, designing new ones, generating synthetic data, and potentially using active learning and grounding LLMs for business use cases. Collaboration with engineers and product managers is key, with an emphasis on publishing research.

Senior Applied Scientist role focused on developing and launching foundation models for content understanding and customer behavior prediction within Prime Video. The role involves hands-on machine learning, research leadership, and end-to-end ownership of solutions, with an emphasis on publishing research findings.

Applied Scientist II at Amazon Prime Video focusing on personalization and discovery. The role involves developing foundation models for content understanding (video, text) and customer behavior prediction using deep learning and multimodal techniques. Responsibilities include building time sequence models, end-to-end solution implementation with engineers and product managers, designing and conducting A/B experiments, and publishing research findings. The team works on recommendation science for Prime Video surfaces and devices, aiming to solve cold-start problems and discover niche customer interests.

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.

This role leads the science strategy and technical vision for an intelligence layer using GenAI and predictive modeling, focusing on heterogeneous signals to power talent applications at Amazon scale. The Principal Applied Scientist will guide a team, conduct hands-on research in areas like foundation models and multi-modal LLMs, design novel ML architectures, and mentor scientists while contributing technically to complex problems.

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.

Principal Applied Scientist role focused on AI-powered design automation for AWS data centers. The role involves defining research roadmaps, developing and deploying ML models (including fine-tuning foundation models, GNNs, NLP, RL, CV) for BIM and AECO applications, and publishing research findings. It requires a blend of theoretical ML knowledge and practical application in a domain with high trust requirements.

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 on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery of solutions impacting international customers.

Applied Scientist II on the Alexa International Team at Amazon, focusing on developing novel algorithms and modeling techniques for Large Language Models (LLMs) and multimodal systems. The role involves fine-tuning/post-training LLMs, building evaluation metrics, and contributing to end-to-end delivery from research to production, impacting international customers with digital assistant technology.

This internship focuses on research in Reinforcement Learning and Optimization within Machine Learning, developing and implementing novel algorithms for complex real-world challenges. The role involves working with large-scale data and applying cutting-edge ML techniques.

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.

Research Scientist role focused on applying and extending state-of-the-art research in NLP, multi-modal modeling, domain adaptation, continuous learning, and large language models to improve product compliance and safety at Amazon. The role involves researching and evaluating algorithms, designing new algorithms for business impact (e.g., synthetic data generation, active learning, grounding LLMs), and collaborating with engineering and product teams to implement ML solutions across the product catalog. The team specializes in image and document understanding for compliance capabilities, with a focus on publishing research.

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.

This internship focuses on bridging cutting-edge AI research with practical application and communication. The intern will translate complex AI concepts into understandable content for business stakeholders and the wider community, document AI capabilities, develop internal AI literacy programs, and contribute to applied research projects in NLP, Computer Vision, or Multimodal AI. The role requires a strong foundation in ML/DL, Python, and ML frameworks, with a passion for science communication and a curious, open mindset.

Applied Scientist role focused on researching and developing NLP, multi-modal, and LLM-based ML solutions for product compliance and safety at Amazon. The role involves evaluating state-of-the-art algorithms, designing new ones, generating synthetic data, and improving grounding of LLMs for business use cases. It requires collaboration with engineers and product managers, and publishing research.

Applied Scientist role focused on researching and developing NLP, multi-modal, and LLM-based ML solutions for product compliance and safety at Amazon. The role involves evaluating state-of-the-art algorithms, designing new ones, generating synthetic data, and improving grounding of LLMs for business use cases. It requires collaboration with engineers and product managers, and publishing research.

Research Scientist role focused on building and deploying large-scale foundation models for demand forecasting at Amazon. The role involves designing experiments, developing deep learning and statistical models, and analyzing large datasets to improve forecasting accuracy and downstream business impact. Emphasis on research rigor, production deployment, and scientific contribution.