Careers

Alexa Connections is on a mission to become the world's most trusted communication agent — spanning calls, text, email, and the ever-expanding surfaces where people connect. We're building intelligence that keeps customers connected effortlessly while putting their trust and privacy first. As an Applied Scientist, you'll help build the smartest communications agent for people — an agent that understands intent, context, and relationships, and that acts on a customer's behalf to make every interaction feel effortless and genuinely helpful Key job responsibilities You'll research, develop, and deploy the next generation of on-device AI for communication, bringing small language models, agentic capabilities, and generative intelligence to the edge so that customers get responsive, and private experiences without relying on the cloud. In practice, that means designing on-device arbitration systems that intelligently route between local and cloud inference in real time, applying modern compression techniques like quantization, distillation, and low-rank adaptation to fit frontier-class capabilities into tight memory and latency budgets, and optimizing inference across on-device accelerators such as NPUs and mobile GPUs. You'll build privacy-first personalization that learns and adapts without user data ever leaving the device, and contribute to the science of edge communication intelligence, intent understanding, contact ranking, proactive connectivity, and conversational reasoning, all running locally. Throughout, you'll collaborate closely with hardware, systems, and product teams to co-design models with the target device in mind, and translate research advances into production features that push the state of the art in on-device AI. About the team We're building up the Alexa Connections science team, so you'll join early and grow alongside it. You'll work closely with experienced scientists and engineers invested in your development, with plenty of mentorship, room to broaden your skills, and the chance to own meaningful problems as you grow. It's a supportive place to do impactful work early in your career, shaping how millions of customers connect every day.

We are seeking a Human-Robot Interaction (HRI) Applied Scientist to develop cutting-edge interactions that make robots feel alive, personal, and fun. In this role, you will focus on verbal and non-verbal conversational systems, social dynamics, memory, and long-term relationship formation between robots, their environments, and the people they interact with. Your contributions will be essential in advancing robotics by enabling expressive, socially intelligent, and trustworthy interactions between robots and humans. Key job responsibilities - Develop interactive systems that leverage large language models, multimodal inputs and outputs, reinforcement learning from human feedback, or other advanced techniques to achieve fluid, engaging, and socially appropriate robot behavior - Design and implement intelligent conversational systems that handle turn-taking, grounding, interruption, and incorporates context drawn from a robot's physical environment and shared history with a user - Integrate perceptual sensor streams including gaze, facial expression, gesture, posture, and more to understand social context and produce coherent, lifelike interactions. - Develop memory and personalization systems that allow robots to form lasting relationships with individual users, learn their environments, and adapt their behavior over weeks and months - Stay updated on advancements in HRI, NLP, multimodal AI, and cognitive and social science to apply cutting-edge techniques to robot interaction challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation

Every product a customer returns is a moment where Amazon either recovers value or writes it off — and India's ReCommerce business is on a multi-million-dollar mission to recover more of it, more intelligently, at scale. Machine learning is the core lever: predicting whether a returned unit is sellable without a human touching it, detecting damage and fraud inside sealed packaging from images, routing each unit to its highest-value disposition, and pricing recovered inventory dynamically. India's returns network is large, fast-growing, and structurally different from other geographies — a rich, high-impact environment for an Applied Scientist to build models that move real financial and customer-experience metrics. We are hiring an Applied Scientist to build and adapt the ML that powers India ReCommerce. You will work at the intersection of two mandates: building India-first models for problems unique to our market, and adapting proven Worldwide models to India's data, catalog, and operational reality — recalibrating them where distribution, language, and process differ. You will own problems end-to-end, from framing and data through modeling, evaluation, and production deployment, partnering closely with engineering, product, and operations. Key job responsibilities Build ML models for automated returns grading — predicting the salability of returned units from structured and unstructured signals so units can be evaluated with zero or minimal human touch, improving speed, accuracy, and recovery value. Develop computer-vision models for defect detection, condition assessment, and anomaly/fraud identification (including inside sealed packaging), and for establishing chain-of-custody and damage attribution across the returns journey. Build disposition-prediction and routing models that direct each unit to its highest-value recovery path (resale, repair, liquidation, donation, recycle) as early as possible in the network. Develop pricing and recovery-optimization models for liquidation and resale, moving from flat rates toward dynamic, grade- and condition-aware pricing. Adapt Worldwide ML models to India — retraining, recalibrating, and re-evaluating for India's return distribution, catalog, languages, and operational constraints, and closing the gaps that prevent a direct lift-and-shift. Own the full model lifecycle — problem framing, data pipelines, feature engineering, training, offline/online evaluation, monitoring, and retraining — with rigorous attention to calibration, drift, and business-metric impact. Partner cross-functionally with engineering (to productionize), product (to frame problems and measure impact), and operations (to ground models in how the network actually runs), and use modern GenAI/LLM tooling to accelerate research and delivery. A day in the life You start by reviewing the performance of a grading model in production — checking calibration and drift against last week's returns, and confirming the recovery-value lift is holding. Mid-morning, you dig into a computer-vision problem: improving detection of a damage type that's driving write-offs, using images captured across the returns journey. In the afternoon you work with a Worldwide science team to bring one of their models to India — scoping what retraining and recalibration India's data requires — then pair with an engineer to move your latest model toward production behind a clean evaluation gate. You close by framing a new problem with a product partner: quantifying the opportunity, defining the label and success metric, and sketching the modeling approach. About the team India ReCommerce owns the systems and science that turn returned and unsellable inventory into recovered value and a better customer experience. You will join a team building an increasingly automated, ML-driven returns network — leveraging Worldwide platforms where they fit and building India-first capabilities where they don't. It is a high-ownership environment with a direct line from your models to measurable business and customer outcomes.

Prime Video is an industry leading, high-growth business and a critical driver of Amazon Prime subscriptions, which contributes to customer loyalty and lifetime value. Prime Video is a digital video streaming and download service that offers Amazon customers the ability to rent, purchase or subscribe to a huge catalog of videos. In addition, Prime Video offers a variety of live sport streaming services in multiple locales. The Prime Video Economist team is looking for an Economist to support PV content valuation. As an economist focusing on Prime Video, you will be responsible for understanding the value that the business creates for our customers and to develop new, disruptive innovations to grow global Prime Video usage and customer value. This role requires an individual with strong quantitative modeling skills and the ability to apply statistical/machine learning, structural models, and experimental design methods to large amount of individual level data. The candidate should have strong communication skills, be able to work closely with stakeholders and translate data-driven findings into actionable insights. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and ability to work in a fast-paced and ever-changing environment. Key job responsibilities The candidate's responsibilities will include: - Build scalable analytic solutions using state of the art tools based on large datasets - Build causal inference models, conduct statistical/machine learning analyses, or design experiments to measure the value of the business and its many features - Partner closely with Business, Finance, Science, and Tech partners to build prototypes and implement production solutions - Independently identify new opportunities for leveraging economic insights and models in the Video business - Develop and execute product workplans from concept, prototype to production incorporating feedback from customers, scientists and business leaders - Write both technical white papers and business-facing documents to clearly explain complex technical concepts to audiences with diverse business/scientific backgrounds

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced ML systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real-world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning team for India Consumer Businesses. Machine Learning, Big Data and related quantitative sciences have been strategic to Amazon from the early years. Amazon has been a pioneer in areas such as recommendation engines, ecommerce fraud detection and large-scale optimization of fulfillment center operations. As Amazon has rapidly grown and diversified, the opportunity for applying machine learning has exploded. We have a very broad collection of practical problems where machine learning systems can dramatically improve the customer experience, reduce cost, and drive speed and automation. These include product bundle recommendations for millions of products, safeguarding financial transactions across by building the risk models, improving catalog quality via extracting product attribute values from structured/unstructured data for millions of products, enhancing address quality by powering customer suggestions We are developing state-of-the-art machine learning solutions to accelerate the Amazon India growth story. Amazon India is an exciting place to be at for a machine learning practitioner. We have the eagerness of a fresh startup to absorb machine learning solutions, and the scale of a mature firm to help support their development at the same time. As part of the India Machine Learning team, you will get to work alongside brilliant minds motivated to solve real-world machine learning problems that make a difference to millions of our customers. We encourage thought leadership and blue ocean thinking in ML. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced ML systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real-world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning team for India Consumer Businesses. Machine Learning, Big Data and related quantitative sciences have been strategic to Amazon from the early years. Amazon has been a pioneer in areas such as recommendation engines, ecommerce fraud detection and large-scale optimization of fulfillment center operations. As Amazon has rapidly grown and diversified, the opportunity for applying machine learning has exploded. We have a very broad collection of practical problems where machine learning systems can dramatically improve the customer experience, reduce cost, and drive speed and automation. These include product bundle recommendations for millions of products, safeguarding financial transactions across by building the risk models, improving catalog quality via extracting product attribute values from structured/unstructured data for millions of products, enhancing address quality by powering customer suggestions We are developing state-of-the-art machine learning solutions to accelerate the Amazon India growth story. Amazon India is an exciting place to be at for a machine learning practitioner. We have the eagerness of a fresh startup to absorb machine learning solutions, and the scale of a mature firm to help support their development at the same time. As part of the India Machine Learning team, you will get to work alongside brilliant minds motivated to solve real-world machine learning problems that make a difference to millions of our customers. We encourage thought leadership and blue ocean thinking in ML. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide

Amazon is hiring a Senior Applied Scientist within Customer Forecasting and Valuation (CFV). CFV owns several of the primary decision metrics Amazon uses to evaluate launches and investments — causal estimates of how customer actions today translate into customer value over the year ahead. These metrics are how Amazon works backwards from the customer at scale: they let thousands of launch decisions a year, across Retail, Ads, Marketing, and Selection, weigh short-term profitability against long-term growth. We are in the middle of a generational rebuild of how these metrics are produced. Our team is developing transformer-based foundation models of customer behavior, learned directly from billions of behavioral events. They are being built as shared infrastructure: one learned representation of customer behavior that a wide range of measurement and optimization systems across Amazon can be built on top of. CFV is part of the Customer Behavior Analytics (CBA) organization, which builds the tools used to understand customer behavior and value generation across Amazon's Retail business. As a Senior Applied Scientist you are working at the intersection of causal inference, sequence modeling, and experimentation. The work spans methodological invention — making learned representations estimation-aware, closing the loop between experimental ground truth and model training, extrapolating short-horizon observations into year-ahead causal effects — and translation of that work into production systems that move real launch decisions. You will partner with other senior scientists, product owners, and business leaders, and you will work closely with a dedicated engineering team. The right candidate has deep expertise in ML and causal inference, judgment about when to invent versus reuse, and the appetite to operate in ambiguity. If you want to be part of a team that is shaping how Amazon measures the value of what it does for customers, and how it balances short-term profitability with long-term growth, we should talk.

The Worldwide Design Engineering (WWDE) organization delivers innovative, effective and efficient engineering solutions that continually improve our customers’ experience. WWDE optimizes designs throughout the entire Amazon value chain providing overall fulfillment solutions from order receipt to last mile delivery. We are seeking a Sr. Simulation Scientist to assist in designing and optimizing the fulfillment network concepts and process improvement solutions using discrete event simulations for our World Wide Design Engineering Team. Successful candidates will be technical expert and natural self-starter who have the drive to apply simulation and optimization tools to solve complex flow and buffer challenges during the development of next generation fulfillment solutions. The Simulation Scientist is expected to deep dive into complex problems and drive relentlessly towards innovative solutions working with cross functional teams. Be comfortable interfacing and influencing various functional teams and individuals at all levels of the organization in order to be successful. Lead strategic modelling and simulation projects related to system simulation, optimization, AI powered simulation, advanced simulation tools development, coding simulation algorithms to drive process design decisions. Key job responsibilities - You influence the scientific strategy across multiple teams in your business area. You support go/no-go decisions, build consensus, and assist leaders in making trade-offs. You proactively clarify ambiguous problems, scientific deficiencies, and where your team’s solutions may bottleneck innovation for other teams. - Lead the design, implementation, and delivery of the simulation science solutions to perform system of systems discrete event simulations for significantly complex operational processes that have a long-term impact on a product, business, or function using FlexSim, Demo 3D, AnyLogic or any other Discrete Event Simulation (DES) software packages, AI powered simulation tools, PLC/IPC Emulations, Robotic system simulation and emulation - Lead strategic system modelling and simulation projects using DES tools, software, PLC/IPC emulations, to drive process design decisions - Be an exemplary practitioner in simulation data science discipline to establish best practices and simplify problems to develop discrete event simulations faster with higher standards - Identify and tackle intrinsically hard process flow simulation problems (e.g., highly complex, ambiguous, undefined, with less existing structure, or having significant business risk or potential for significant impact - Deliver artifacts that set the standard in the organization for excellence, from process flow control algorithm design to validation to implementations to technical documents using simulations - Be a pragmatic problem solver by applying judgment and simulation experience to balance cross-organization trade-offs between competing interests and effectively influence, negotiate, and communicate with internal and external business partners, contractors and vendors for multiple simulation projects - Provide simulation data and measurements that influence the business strategy of an organization. Write effective white papers and artifacts while documenting your approach, simulation outcomes, recommendations, and arguments - Lead and actively participate in reviews of simulation data science solutions. You bring clarity to complexity, probe assumptions, illuminate pitfalls, and foster shared understanding within simulation data science discipline - Pay a significant role in the career development of others, actively mentoring and educating the larger simulation data science community on trends, technologies, and best practices - Use advanced statistical /simulation tools and develop codes (python or another object oriented language) for research , simulation, and modeling algorithms - Lead and coordinate simulation efforts between internal teams and outside vendors to develop optimal solutions for the network, including equipment specification, material flow control logic, process design, and site layout - Deliver results according to project schedules and quality A day in the life The day-to-day activities include challenging and problem solving scenario with fun filled environment working with talented and friendly team members. The stakeholders includes Worldwide Design Engineering org verticals, Fulfillment Technology and Robotics team members. The team solve problems related to critical design automation of Material handling equipment and technology design solutions. Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team WWDE Simulation Team’s mission is to apply advanced simulation tools and techniques to drive process flow design, optimization, and improvement for the Amazon Fulfillment Network. We take ownership and pride to come up with simulation based solutions to drive the designs and earn the customer trust. We always strive to invent new ways to simulate and optimize the design and to invent design solutions. We help design and operation engineers to make simulation driven decisions by producing detailed math models and generating key performance metrics.

The Worldwide Design Engineering (WWDE) organization delivers innovative, effective and efficient engineering solutions that continually improve our customers’ experience. WWDE optimizes designs throughout the entire Amazon value chain providing overall fulfillment solutions from order receipt to last mile delivery. We are seeking a Simulation Scientist to assist in designing and optimizing the fulfillment network concepts and process improvement solutions using discrete event simulations for our World Wide Design Engineering Team. Successful candidates will be technical expert and natural self-starter who have the drive to apply simulation and optimization tools to solve complex flow and buffer challenges during the development of next generation fulfillment solutions. The Simulation Scientist is expected to deep dive into complex problems and drive relentlessly towards innovative solutions working with cross functional teams. Be comfortable interfacing and influencing various functional teams and individuals at all levels of the organization in order to be successful. Lead strategic modelling and simulation projects related to drive process design decisions. Key job responsibilities - You influence the scientific strategy across multiple teams in your business area. You support go/no-go decisions, build consensus, and assist leaders in making trade-offs. You proactively clarify ambiguous problems, scientific deficiencies, and where your team’s solutions may bottleneck innovation for other teams. - Lead the design, implementation, and delivery of the simulation data science solutions to perform system of systems discrete event simulations for significantly complex operational processes that have a long-term impact on a product, business, or function using FlexSim, Demo 3D, AnyLogic or any other Discrete Event Simulation (DES) software packages - Lead strategic modeling and simulation research projects to drive process design decisions - Be an exemplary practitioner in simulation science discipline to establish best practices and simplify problems to develop discrete event simulations faster with higher standards - Identify and tackle intrinsically hard process flow simulation problems (e.g., highly complex, ambiguous, undefined, with less existing structure, or having significant business risk or potential for significant impact - Deliver artifacts that set the standard in the organization for excellence, from process flow control algorithm design to validation to implementations to technical documents using simulations - Be a pragmatic problem solver by applying judgment and simulation experience to balance cross-organization trade-offs between competing interests and effectively influence, negotiate, and communicate with internal and external business partners, contractors and vendors for multiple simulation projects - Provide simulation data and measurements that influence the business strategy of an organization. Write effective white papers and artifacts while documenting your approach, simulation outcomes, recommendations, and arguments - Lead and actively participate in reviews of simulation research science solutions. You bring clarity to complexity, probe assumptions, illuminate pitfalls, and foster shared understanding within simulation data science discipline - Pay a significant role in the career development of others, actively mentoring and educating the larger simulation data science community on trends, technologies, and best practices - Use advanced statistical /simulation tools and develop codes (python or another object oriented language) for data analysis , simulation, and developing modeling algorithms - Lead and coordinate simulation efforts between internal teams and outside vendors to develop optimal solutions for the network, including equipment specification, material flow control logic, process design, and site layout - Deliver results according to project schedules and quality A day in the life The dat-to-day activities include challenging and problem solving scenario with fun filled environment working with talented and friendly team members. The internal stakeholders are IDEAS team members, WWDE design vertical and Global robotics team members. The team solve problems related to critical Capital decision making related to Material handling equipment and technology design solutions. Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team World Wide Design EngineeringSimulation Team’s mission is to apply advanced simulation tools and techniques to drive process flow design, optimization, and improvement for the Amazon Fulfillment Network. Team develops flow and buffer system simulation, physics simulation, package dynamics simulation and emulation models for various Amazon network facilities, such as Fulfillment Centers (FC), Inbound Cross-Dock (IXD) locations, Sort Centers, Airhubs, Delivery Stations, and Air hubs/Gateways. These intricate simulation models serve as invaluable tools, effectively identifying process flow bottlenecks and optimizing throughput.

Twitch is looking for an Applied Scientist to lead computer vision and video manipulation work within Amazon Publisher Monetization (APM). You will apply large language models (LLMs), vision-language models (VLMs), and traditional computer vision techniques to build products that directly improve how millions of Twitch creators produce content and how viewers engage with it. This is a high-visibility project backed by senior leadership, and you will have the autonomy to evaluate both first-party and third-party solutions to deliver the highest quality results quickly. If you thrive in a fast-paced, startup-like environment where your scientific decisions shape the product roadmap, we want to hear from you. Key job responsibilities Design and deploy computer vision and video manipulation models using LLMs, VLMs, and traditional techniques, taking solutions from research prototype to production. Partner with senior Product and Twitch leadership to translate customer needs into well-defined scientific problems and deliver end-to-end solutions. Evaluate and integrate generative AI capabilities across Amazon Bedrock and third-party platforms, selecting the approach that maximizes product quality. Drive the team's scientific agenda by proposing new initiatives, authoring technical documents, and contributing to external publications. Mentor fellow scientists and engineers through code reviews, design guidance, and technical assessments that raise the overall quality bar. A day in the life You will split your time roughly evenly between independent research and collaborative sessions. Mornings often start with focused solo work — benchmarking a new VLM, writing model code, or analyzing experiment results. Midday you join working sessions with Product and Twitch leadership to align on requirements or review progress. Afternoons shift between pairing with engineers on production integration and mentoring teammates on evaluation methodology. Weekly science reviews and bi-weekly roadmap syncs keep the team aligned without over-scheduling your calendar. About the team The APM science team supports all of Amazon's Publisher Monetization efforts, building the models and systems behind Twitch's creator tools and ad experiences. The team includes applied scientists, software engineers, and product managers who collaborate daily to move ideas from whiteboard to production. We operate with a startup mentality — moving fast, wearing multiple hats, and iterating closely with Twitch and Product partners. We are accelerating investment in computer vision and generative AI, and this role will be central to that expansion.