Careers

Alexa International is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs), ASR, TTS, and Speech to Speech models, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Alexa's international products and services. Key job responsibilities As an Applied Scientist with the Alexa International team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with Large Language Models (LLMs), ASR, TTS, and Speech to Speech model. Your work will directly impact our international customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Drive research in ASR, TTS, and Speech-to-Speech (S2S) model training and fine-tuning * Advance multilingual speech recognition and synthesis using LLM-based architectures * Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF. * Collaborate with partner teams on evaluation frameworks and post-training methodologies. * Communicate solutions clearly to partners and stakeholders. * Contribute to the scientific community through publications and community engagement.

As an Applied Scientist on the Science SW team, you will be a versatile generalist who collaborates closely with other scientists and engineers teams to bring research to production across a broad portfolio of problems: from computer-vision perception platforms to building-wide optimization and orchestration. This role combines the scientific application of ML and applied mathematics with a strong product focus. It will be your job to frame ambiguous business problems as tractable scientific problems, and to implement novel ML systems, first-principles models, embedded systems prototypes, and performance optimizations in both prototype and production environments. Key job responsibilities • Own the research and development of scientific and ML solutions across a broad range of problems spanning classical machine learning, statistical modeling, computer vision, optimization, and physics-informed / first-principles modeling in a production environment. • Rapidly ramp on unfamiliar problem domains, frame ambiguous or open-ended business problems as tractable scientific problems, and prototype solutions end to end. • Prototype and evaluate sensing hardware and lightweight, edge-deployable models that run on commodity compute under real-world constraints. • Collaborate across multiple science and engineering teams to integrate your solutions into our deployment architecture. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.

The Alexa Edge AI team is seeking a talented and motivated Applied Scientist to join our newly established team in Bangalore. In this role, you will design, develop, and deploy state-of-the-art machine learning models spanning computer vision (CV), audio (including speech) processing, and multimodal semantic understanding for both edge and cloud deployment. You will work at the intersection of multiple modalities to build systems that can perceive, interpret, and reason about the world — pushing the boundaries of what's possible in unified multimodal intelligence. This is a unique opportunity to be a founding member of a brand-new site, shaping the team culture, technical direction, and research agenda from the ground up. Key job responsibilities Model Development: Design and build deep learning models for computer vision, audio understanding, and multimodal semantic fusion — including architectures that enable joint reasoning across visual, auditory, and textual modalities. End-to-End Ownership: Own the full ML lifecycle — from problem formulation, data strategy, and annotation design through experimentation, evaluation frameworks, model optimization, and deployment at scale. Research & Innovation: Stay at the frontier of CV, audio ML, and multimodal learning; identify and apply SOTA techniques and contribute to the scientific community through papers at top-tier venues (CVPR, NeurIPS, ICASSP, ICCV, ACL). Mentorship & Culture Building: As a founding member of the Bangalore site, help hire, onboard, and establish the technical practices that define the team's culture. A day in the life An Applied Scientist with the Alexa Edge AI team will support science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience; while setting examples for the team on good science practice and standards. Besides theoretical analysis and innovation, an Applied Scientist will also work closely with talented engineers and scientists to put algorithms and models into production. About the team The Alexa Edge AI team has a mission to deliver best in class, resource efficient multimodal AI models in support of various perception (vision, audio and speech) and semantic understanding based applications for devices like Echo Show series within Amazon.

Amazon Ads Brand Safety & Suitability protects advertisers from exposure to unsafe, unsuitable, or policy-violating content across web, mobile app, CTV, and audio advertising inventory. Our mission is to ensure that every ad impression delivered through Amazon's demand-side platform appears adjacent to content that meets advertiser trust expectations while giving brands granular controls to define suitability on their own terms. We operate at the intersection of advertiser trust, publisher quality, and supply integrity. AI is fundamentally changing the content landscape. Content is now generated at unprecedented scale — faster, cheaper, and increasingly sophisticated. Low-quality, deceptive, AI-generated, and synthetic content evolves in real time, constantly adapting to evade detection. The volume and velocity of new content entering the advertising system has outpaced traditional classification approaches. We are looking for an Applied Scientist to work on the next generation of AI-powered Brand Safety and Content Classification systems designed to protect advertisers and elevate supply quality at internet scale. This is not a traditional classification problem. You will build systems that make millisecond-level decisions across billions of content signals while continuously adapting to emerging content risks driven by generative AI. You will own the science strategy for LLM-powered classification and semantic understanding, real-time multimodal content evaluation, adversarial ML and adaptive model resilience, proactive risk intelligence and content risk hunting, AI-generated and synthetic content detection, and large-scale abusive content system identification and disruption. You will define how modern AI separates high-quality advertising inventory from unsafe, unsuitable, and policy-violating content — across web, mobile app, CTV, and audio surfaces. What Makes This Role Unique Generative AI has dramatically lowered the cost of producing deceptive, policy-evasive content, and the adversary evolves daily. Your detection systems must reason contextually, adapt rapidly, and generalize beyond previously seen content risk patterns. Static models fail here; you will build living systems that learn and respond in real time. You will do this at internet scale, developing low-latency ML and LLM-powered systems evaluating content safety, brand suitability, misinformation risk, and emerging content risk vectors across massive real-time traffic streams, making billions of decisions per day with single-digit millisecond latency constraints. This role sits at the intersection of frontier AI research and large-scale production engineering, combining deep science, system-wide impact, and business-critical outcomes. The models your team ships directly influence billions of dollars in advertising spend and the trust of the world's largest brands in Amazon DSP. The Science Problems Are Genuinely Hard You will tackle challenges including detecting sophisticated AI-generated and synthetic content, understanding nuanced contextual brand risk, identifying coordinated MFA space before they scale, balancing precision, recall, latency, explainability, and fairness, designing adaptive models resilient to adversarial evolution, and leveraging LLMs for semantic understanding in real-time, latency-constrained environments. Why This Matters Few roles offer the opportunity to work at the intersection of frontier AI, internet-scale production systems, adversarial environments, and business-critical impact — while tackling open-ended scientific challenges with real-world societal relevance. As AI reshapes the internet, the systems your team builds will define what trustworthy, high-quality digital systems look like for the next decade. Key job responsibilities - Own the science strategy for AI-powered brand safety classification across web, mobile app, CTV, and audio surfaces. - Build LLM-powered content classification systems making billions of decisions/day at single-digit millisecond latency - Develop multimodal evaluation pipelines reasoning across text, images, audio, and video in real time - Design adaptive ML systems resilient to adversarial evolution-- continuously learning rather than relying on static models - Build proactive risk intelligence systems that surface emerging content risk vectors through automated hunting - Develop semantic understanding for nuanced contextual brand risk. - Balance precision, recall, latency, explainability, and fairness at internet scale - Define measurement frameworks and drive continuous improvement - Translate research into production — own the path from prototype to deployed model - Publish at peer-reviewed venues; contribute to the scientific community in adversarial ML, NLP, and content safety

The Music Catalog Quality team at Amazon Music serves a key role in developing solutions to ensure and improve the quality of catalog metadata and content across the music streaming experience. We create solutions that detect, measure, and remediate quality issues in music metadata - including artist information, track attributes, versions, content tags, and provide actionable insights that enable continuous improvement of the catalog. We leverage a host of scientific and engineering technologies to accomplish this mission, including Generative AI, classical ML, Natural Language Processing, Computer Vision, and automated data validation pipelines. As an Applied Science Manager on the team, you will lead a team of scientists to define and execute a transformative vision for holistic catalog quality measurement, metadata enrichment, and content integrity. Your team will own the science solutions for foundational quality detection frameworks, metadata validation and correction technologies, state-of-the-art algorithms to identify and resolve catalog anomalies (violative content, duplicative/low value content, misattributed tracks, incorrect metadata), and/or agentic AI solutions that help internal teams quickly surface and fix quality issues to ensure customers receive accurate, complete catalog experiences. Key job responsibilities You independently manage a team of scientists. You identify the needs of your team and effectively grow, hire, and promote scientists to maintain a high-performing team. You have a broad understanding of scientific techniques, several of which may fall out of your specific job function. You define the strategic vision for your team. You establish a roadmap and successfully deliver scientific solutions that innovate on catalog quality detection, metadata enrichment, and content integrity. You define clear goals for your team and effectively prioritize, balancing short-term quality improvements and long-term innovation in catalog intelligence. You establish clear and effective metrics and scientific process to enforce consistent, high-quality artifact delivery and measurable catalog quality improvements. You proactively identify risks and bring them to the attention of your manager, customers, and stakeholders with plans for mitigation before they become roadblocks. You know when to escalate. You communicate ideas effectively, both verbally and in writing, to all types of audiences. You author strategic documentation for your team. You communicate issues and options with leaders in such a way that facilitates understanding and that leads to a decision. You work successfully with customers, leaders, and engineering teams. You foster a constructive dialogue, harmonize discordant views, and lead the resolution of contentious issues. About the team We are a team of scientists and MLEs focused on music catalog quality and metadata intelligence. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and data quality systems with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, content operations teams, and other scientists with expertise in music metadata, content classification, and building scalable modeling and software solutions that keep the Amazon Music catalog accurate, complete, and trustworthy.

Do you want to join an innovative team of scientists and engineers who use terabytes of data and create state-of-the-art Generative AI algorithms to push the boundaries of AI creativity? We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs and Large Model training techniques that fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge. We are looking for scientists who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM and Large Model training techniques. On a day-to-day basis, you will: - Research and implement new algorithms and architectures for generative AI applications. - Optimize model performance and scalability for inference and deployment. - Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation. - Experiment with SOTA methods to improve generative AI model quality. - Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.

Do you want to join an innovative team of scientists and engineers who use terabytes of data and create state-of-the-art Generative AI algorithms to push the boundaries of AI creativity? We are building foundational behavioral models for Amazon Stores using Generative AI, LLMs and Large Model training techniques that fuses general world knowledge, customer shopping behavior and Amazon e-commerce domain knowledge. We are looking for scientists who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry using intelligent and transformative AI applications. Working closely with cross-functional teams, you will be an essential part of every stage of AI development, from ideation and design to rigorous testing and successful deployment, ensuring our AI projects drive innovation and provide value for our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities In this role you will leverage your background and expertise to lead developing foundational behavioral model for Amazon Stores using Generative AI, LLM and Large Model training techniques. On a day-to-day basis, you will: - Research and implement new algorithms and architectures for generative AI applications. - Optimize model performance and scalability for inference and deployment. - Collaborate with other talented applied scientists and engineers to gather and preprocess large datasets and develop an improved training infrastructure that accelerates innovation. - Experiment with SOTA methods to improve generative AI model quality. - Provide technical expertise and guidance to support the integration of generative AI solutions into various products and services.

Amazon’s Marketplace business is one of the largest in the world operating in 23 countries. Amazon Marketplace enables millions of Sellers worldwide to list hundreds of millions of products and manage orders for inventory across dozens of different categories and languages. The IN Seller Partner Services Tech organization is responsible for building delightful seller experiences with the vision of being the technology foundation that makes Amazon the #1 choice for sellers of all types and price-sensitive customers in India and emerging markets—transforming how a billion people buy and sell, thereby transforming lives. By making its sellers successful, Amazon can attract and retain sellers on the platform, which in turn offers its customers the best selection, convenience, pricing and experience resulting in multi-billion-dollar business. This organization enables sellers of all stripes across a multitude of business lines existing or new such as Q-Commerce, Pharmacy and Out of country selling. We are looking for a motivated and innovative Applied Scientist with strong analytical skills and practical experience to join our science team. As a key member of our science team, you will provide expertise that helps accelerate the business and make it profitable. You will research, design and improve on the models that will directly impact Amazon’s Selection quality and maximize fee revenue. You will be working in a highly collaborative environment partnering with various science, product management, engineering, operations, finance, business intelligence and analytics teams. You will need to understand the business requirements and translate them into complex analytical outputs. You will design tests to explain performance of the models from impact on customer and cost perspective. You will create ML models to capture features impacting performance. You should be comfortable building prototypes, testing and improving them given the feedback from the real time data. You should be able to present your model and findings to a various range of stakeholders. Looking for candidate with expertise in the areas of machine learning, operations research, and statistics. With expertise in applying theoretical models in an applied environment relying heavily on the latest advances in machine learning, optimization, stochastic modeling, and engineering. The candidate will be expected to work on numerous aspects, such as feature engineering, modeling, probabilistic modeling, hyper-parameter tuning, scalable inference methods and latent variable models. Challenges will involve dealing with very large data sets and requirements on throughput. Key job responsibilities - Design, implement, test, deploy, and maintain innovative science solutions to accelerate our business. - Create experiments and prototype implementations of new learning algorithms and prediction techniques - Collaborate with scientists, engineers, product managers, and stakeholders to design and implement software solutions for science problems - Use best practices to ensure a high standard of quality for all of the team deliverables

Employer: Amazon.com Services LLC Position: Applied Scientist II - AMZ27580.1 Location: Nashville, TN Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. (40 hours / week, 8:00am-5:00pm, Salary Range $136000 - $184000) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000

At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our mission is to prevent denied entities from transacting with Amazon businesses. We build automatic mechanisms to detect and prevent prohibited transactions with denied entities using a diverse set of algorithms and machine learning techniques. We screen over a billion events every day and develop algorithms which are able to scale and detect suspicious entities . We are still Day 1 and have an exciting road map to build Machine Learning (ML) and Generative AI (LLM) powered detection and resolution systems to help scale Amazon for years to come. We are seeking an Sr. Applied Scientist to join our team and help tackle challenging problems at the forefront of machine learning and artificial intelligence. Working closely with a multidisciplinary team of engineers, data scientists, and domain experts, you will play a crucial role in defining innovative ML/AI-powered customer experiences and solutions. If you have an entrepreneurial mindset, the technical depth to deliver impactful results, and a passion for innovation, we want to hear from you. Key job responsibilities In this role, you will: • Drive the research, design, and development of novel ML/AI models and systems to power critical products and services • Collaborate cross-functionally to deeply understand business requirements, customer needs, and technical constraints • Rapidly prototype, test, and iterate on ML/AI solutions, iterating quickly based on data and feedback • Communicate complex technical concepts to technical and non-technical stakeholders • Mentor and grow a team of talented ML scientists and engineers • Stay up-to-date on the latest advancements in AI/ML and identify opportunities to apply emerging techniques A day in the life - Starting the day by reviewing the latest model performance metrics and identifying areas for improvement - Brainstorming new architectures and approaches with your cross-functional team during a whiteboard session - Diving deep into a complex dataset, leveraging advanced statistical and AI techniques to uncover hidden insights - Prototyping a new model and running a series of experiments to optimize its performance - Preparing a presentation to pitch your latest research findings and recommendations to product and engineering leaders - Mentoring junior scientists, providing guidance on coding best practices and problem-solving strategies About the team Why Amazon Security At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training and Career growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional.