Social
Rohit re-MARS.png
Alexa AI senior vice president and head scientist Rohit Prasad onstage at re:MARS 2022.
Conversational AI

Alexa's head scientist on conversational exploration, ambient AI

Rohit Prasad on the pathway to generalizable intelligence and what excites him most about his re:MARS keynote.

By Staff writer
June 22, 2022
7 min read
Share

In a talk today at re:MARS — Amazon’s conference on machine learning, automation, robotics, and space — Rohit Prasad, Alexa AI senior vice president and head scientist, discussed the emerging paradigm of ambient intelligence, in which artificial intelligence is everywhere around you, responding to requests and anticipating your needs, but fading into the background when you don’t need it. Ambient intelligence, Prasad argued, offers the most practical route to generalizable intelligence, and the best evidence for that is the difference that Alexa is already making in customers’ lives.

Amazon Science caught up with Prasad to ask him a few questions about his talk.

  1. Q. 

    What is ambient intelligence?
    A. 

    Ambient intelligence is artificial intelligence [AI] that is embedded everywhere in our environment. It is both reactive, responding to explicit customer requests, and proactive, anticipating customer needs. It uses a broad range of sensing technologies, like sound, vision, ultrasound, atmospheric sensing like temperature and humidity, depth sensors, and mechanical sensors, and it takes actions, playing your favorite tune, looking up information, buying products you need, or controlling thermostats, lights, or blinds in your smart home.

    Doppler-Illustration.gif
    Related content
    The science behind ultrasonic motion sensing for Echo
    Reducing false positives for rare events, adapting Echo hardware to ultrasound sensing, and enabling concurrent ultrasound sensing and music playback are just a few challenges Amazon researchers addressed.

    Ambient intelligence is best exemplified by AI services like Alexa, which we use on a daily basis. Customers interact with Alexa billions of times each week. And thanks to predictive and proactive features like Hunches and Routines, more than 30% of smart-home interactions are initiated by Alexa.

  2. Q. 

    Why does ambient intelligence offer the most practical route to generalizable intelligence?
    A. 

    Alexa is made up of more than 30 machine learning systems that can each process different sensory signals. The real-time orchestration of these sophisticated machine learning systems makes Alexa one of the most complex applications of AI in the world.

    30+ ML systems.cropped.png
    Alexa is made up of more than 30 machine learning systems that process different sensory signals.

    Still, our customers demand even more from Alexa as their personal assistant, advisor, and companion. To continue to meet customer expectations, Alexa can’t just be a collection of special-purpose AI modules. Instead, it needs to be able to learn on its own and to generalize what it learns to new contexts. That’s why the ambient-intelligence path leads to generalizable intelligence.

    Generalizable intelligence [GI] doesn’t imply an all-knowing, all-capable, über AI that can accomplish any task in the world. Our definition is more pragmatic, with three key attributes: a GI agent can (1) accomplish multiple tasks; (2) rapidly evolve to ever-changing environments; and (3) learn new concepts and actions with minimal external human input. For inspiration for such intelligence, we don’t need to look far: we humans are still the best example of generalization and the standard for AI to aspire to.

    Absorbing-Markov-chain models for three different sequences of utterances
    Related content
    How we taught Alexa to correct her own defects
    Self-learning system uses customers’ rephrased requests as implicit error signals.

    We’re already seeing some of this today, with AI generalizing much better than ever before. Foundational Transformer-based large language models trained with self-supervision are powering many tasks with significantly less manually labeled data than was required before. For example, our large language model pretrained on Alexa interactions — the Alexa Teacher Model — captures knowledge that is used in language understanding, dialogue prediction, speech recognition, and even visual-scene understanding. We have also proven that models trained on multiple languages often outperform single-language models.

    Another element of better generalization is learning with little or no human involvement. Alexa’s self-learning mechanism is automatically correcting tens of millions of defects — both customer errors and errors in Alexa’s language-understanding models — each week. Customers can teach Alexa new behaviors, and Alexa can automatically generalize them across contexts — learning, for instance, that terms used to describe lighting settings can also be applied to speaker settings.

  3. Q. 

    Generalizing across contexts and reliably predicting customer needs will require more common sense than most AI systems exhibit today. How does common sense fit in to this picture?
    A. 

    To begin with, Alexa already exhibits common sense in a number of areas. For example, if you say to Alexa, “Set a reminder for the Super Bowl”, Alexa not only identifies the Super Bowl date and time but converts it into the customer’s time zone and reminds the customer 10 minutes before the start of the game, so they can wrap up what they are doing and get ready to watch the game.

    Smarthouse Usage Patterns.jpg
    Related content
    The science behind Hunches: Deep device embeddings
    A machine learning model learns representations that cluster devices according to their usage patterns.

    Another example is suggested Routines, where Alexa detects frequent customer interaction patterns and proactively suggests automating them via a Routine. So if someone frequently asks Alexa to turn on the lights and turn up the heat at 7:00 a.m., Alexa might suggest a Routine that does that automatically.

    Even if the customer didn’t set up a Routine, Alexa can detect anomalies as part of its Hunches feature. For example, Alexa can alert you about the garage door being left open at 9:00 p.m., if it's usually closed at that time.

    Moving forward, we are aspiring to take automated reasoning to a whole new level. Our first goal is the pervasive use of commonsense knowledge in conversational AI. As part of that effort, we have collected and publicly released the largest dataset for social common sense in an interactive setting.

    We have also invented a generative approach that we call think-before-you-speak. In this approach, the AI learns to first externalize implicit commonsense knowledge — that is, “think” — using a large language model combined with a commonsense knowledge graph such as ConceptNet. Then it uses this knowledge to generate responses — that is, to “speak”.

    Think-before-you-speak.cropped.png
    An overview of the think-before-you-speak approach.

    For example, if during a social conversation on Valentine’s day a customer says, “Alexa, I want to buy flowers for my wife”, Alexa can leverage world knowledge and temporal context to respond with “Perhaps you should get her red roses”.

    We’re also working to enable Alexa to answer complex queries that require multiple inference steps. For example, if a customer asks, "Has Austria won more skiing medals than Norway?", Alexa needs to combine the mention of skiing medals with temporal context to infer that the customer is asking about the Winter Olympics. Then Alexa needs to resolve “skiing” to the set of Winter Olympics events that involve skiing, which is not trivial, since those events can have names like “Nordic combined” and “biathlon”. Next, Alexa needs to retrieve and aggregate medal counts for each country and, finally, compare results.

    Skiing medals.cropped.png
    The Alexa AI team is working to enable Alexa to answer complex queries that require multiple inference steps.

    A key requirement for responding to such questions is explainability. Alexa shouldn't just reply "yes" but provide a response that summarizes Alexa's inference steps, such as "Norway has won X medals in skiing events in the Winter Olympics, which is Y more than Austria".

  4. Q. 

    What’s the one thing you are most excited about from your re:MARS keynote?
    A. 

    If I had to pick one thing among the suite of capabilities we showed at re:MARS, I’d say it is conversational explorations. Through the years, we have made Alexa far more knowledgeable, and it has gained expertise in many domains of information to answer natural-language queries from customers.

    GraphTraversal_Cropped.gif
    Related content
    New method improves knowledge-graph-based question answering
    Replacing hand annotation with a machine learning component reduces labor, while an intersection operation enables multiple-entity queries.

    Now, we are taking such question answering to the next level. We are enabling conversational explorations on ambient devices, so you don’t have to pull out your phone or go to your laptop to explore information on the web. Instead, Alexa guides you on your topic of interest, distilling a wide variety of information available on the web and shifting the heavy lifting of researching content from you to Alexa.

    The idea is that when you ask Alexa a question — about a news story you’re following, a product you’re interested in, or, say, where to hike — the response includes specific information to help you make a decision, such as an excerpt from a product review. If that initial response gives you enough information to make a decision, great. But if it doesn’t — if, for instance, you ask for other options — that’s information that Alexa can use to sharpen its answer to your question or provide helpful suggestions.

    Making this possible required three different types of advances. One is in dialogue flow prediction through deep learning in Alexa Conversations. The second is web-scale neural information retrieval to match relevant information to customer queries. And the third is automated summarization, to distill information from one or multiple sources.

    Alexa Conversations is a dialogue manager that decides what actions Alexa should take based on customer interactions, dialogue history, and the current query or input. It lets users navigate and select information on-screen in a natural way — say, searching by topics or partial titles. And it uses query-guided attention and self-attention mechanisms to incorporate on-screen context into dialogue management, to understand how users are referencing entities on-screen.

    Serena-still.jpg
    Related content
    Establishing a new standard in answer selection precision
    A model that uses both local and global context improves on the state of the art by 6% and 11% on two benchmark datasets.

    Web-scale neural information retrieval retrieves information in different modalities and in different languages, at the scale of billions of data points. Conversational explorations uses Transformer-based models to semantically match customer queries with relevant information. The models are trained using a multistage training paradigm optimized for diverse data sources.

    And finally, conversational explorations uses deep-learning models to summarize information in bite-sized snippets, while keeping crucial information.

    Customers will soon be able to experience such explorations, and we’re excited to get their feedback, to help us expand and enhance this capability in the months ahead.

    Amazon re:MARS 2022 - Day 2 - Keynote
    43:36 Rohit Prasad, SVP and Head Scientist, Alexa AI, Amazon

Research areas
Tags
About the Author
Staff writer

Related content

Work with us

See more jobs See more jobs
Senior Inference Engineer, AGI
US, CA, Sunnyvale
We are looking for a Senior Inference Engineer to own inference for real-time multimodal conversational AI. This is a full-stack inference role: you will work across the entire path a model takes from research to production — shaping model architecture so it is servable, building the real-time runtime that serves it within hard latency budgets, and building the offline systems that train and reinforce it. You will operate at the boundary of Science and Inference, taking frontier-scale speech and audio models and making them run within real-time latency budgets on production hardware. You will co-design architectures with scientists to make them inference-friendly from inception, own the low-latency streaming serving path, and build the training and reinforcement-learning infrastructure that closes the loop. You will have the compute, data, and runway to solve problems that few teams in the world are positioned to tackle. As a Senior Engineer, you will own a significant area of the inference stack end to end, drive its technical execution, contribute to the team's roadmap, and work closely with scientists and hardware partners to ensure our models run fast enough to feel human in real time — and at a cost that makes them viable at scale. You may go deep in one of the areas below while contributing across the others. Key job responsibilities Model Architecture & Inference Co-Design • Partner with research scientists to make model architectures servable from inception — surfacing the latency, memory, and cost implications of architecture choices before they are locked in • Implement and optimize the inference path for large-scale multimodal models — attention and KV-cache mechanisms, multimodal/autoregressive decoding, and the compute primitives on the critical path Apply efficiency techniques across the stack — quantization (per-tensor/per-channel/per- group, INT8/FP8/BF16), speculative decoding, operator fusion, and paged KV-cache — and quantify their quality/latency trade-offs • Develop and tune high-performance kernels for critical operations where off-the-shelf implementations leave performance on the table, integrating them into production serving with minimal overhead • Profile end-to-end performance with tools such as Nsight Compute/Systems and roofline analysis to identify and eliminate bottlenecks in large-scale inference workloads Real-Time & Interactive Runtime • Own the real-time serving path for streaming multimodal conversational AI, meeting sub- second, streaming latency budgets under concurrent session load • Build and tune continuous batching, scheduling, and preemption to balance throughput against per-request latency SLAs for interactive workloads • Customize production serving frameworks (e.g., vLLM, PyTorch) for real-time streaming generative models that fall outside standard LLM serving patterns — sustained low-latency output under concurrent session load • Implement multi-GPU inference (tensor parallelism, collective communication) for latency- critical paths, and drive cost toward parity with existing production baselines • Establish latency, throughput, and cost benchmarking, and publish the operational metrics that gate deployment Offline Systems: Training, RL & Evaluation Infrastructure • Build and scale the offline inference systems behind post-training — high-throughput rollout generation and reward-model serving for reinforcement learning (RL/RLHF/RLAIF) • Ensure train/serve consistency — that the inference path used in RL and evaluation faithfully matches production online behavior (e.g., parity across sampling and logit processing) • Work with the evaluation team to enable offline inference that captures the quality dimensions unique to real-time conversation — latency sensitivity, audio quality, and interaction naturalness
Applied Scientist, Last Mile Science
US, TX, Austin
Are You Ready to Redefine How the World Receives Its Packages? What if your algorithms defined the most efficient path for millions of deliveries — every single day? At Amazon, we're building the science that makes that possible, and we're looking for exceptional scientists to help lead the way. The Last Mile Routing & Planning organization develops the software, algorithms, and tools that power the "magic" of home delivery. Our planning and routing intelligence systems drive billions of daily decisions — enabling safe, efficient, and frustration-free routes for drivers across the globe. What You'll Do In this role, you'll sit at the intersection of state-of-the-art research and real-world impact. You will: - Design and build algorithms that solve large-scale, complex logistics problems - Synthesize data from diverse sources to identify high-value business opportunities - Provide research direction and data-driven insights to guide strategic decisions - Translate complex technical approaches into clear communication for scientists, engineers, and business stakeholders - Partner closely with scientists and engineers in a collaborative, high-impact environment What You'll Work On We have an exciting and growing portfolio of research areas, including: - Routing for same-day and grocery deliveries - Planning for electric and autonomous vehicles - District-level and stop-level planning - Forecasting solutions for diverse delivery programs All of this is powered by the latest methods in Operations Research (OR), Machine Learning (ML), and Generative AI — at a truly global scale. Successful candidates will lead one or more of these problem spaces. What We're Looking For - Deep expertise in Operations Research and/or Machine Learning methods - Proven experience applying these methods to large-scale, real-world business problems - Ability to translate models into production-ready code in Python or Java - Strong communication skills — you can explain complex technical concepts to diverse audiences - A bias for action and an iterative mindset when tackling ambitious research challenges Why Amazon We're passionate about your growth. Whether you want to explore emerging technologies, take on broader scope, or accelerate your career trajectory, we'll invest in helping you get there. Our business is scaling fast — and so are the opportunities for the people who build it. If you're driven by the challenge of optimizing one of the world's most complex logistics systems and excited to see your work impact millions of customers daily, we'd love to hear from you. Key job responsibilities - Invent and design novel solutions for scientifically complex problem areas, and identify opportunities for invention within existing and new business initiatives - Deliver large-scale, high-impact solutions to complex problems in support of medium-to-large business goals - Shape the design of scientifically complex software systems, personally contributing significant portions of the critical scientific novelty - Apply mathematical optimization, machine learning, and Generative AI techniques to develop solution methodologies for in-house decision support tools and software - Research, prototype, simulate, and experiment with models — and actively participate in their production-level deployment in Python or Java - Engage with the broader scientific community by publishing research articles and participating in leading research conferences
Principal Applied Scientist, Real-Time Conversational AI , AGI
US, WA, Seattle
We are looking for a Principal Applied Scientist to drive the research and development of real-time multimodal conversational AI. You will operate across two focus areas: advancing foundation models for speech and audio, and building the post-training systems (reward modeling, reinforcement learning) that shape natural, human-like conversational behavior. You will be the expert in your area while contributing across the full model lifecycle — from pre-training and architecture design through post-training alignment and real-time deployment. You will work at the frontier of what's possible in conversational AI, with the compute, data, and runway to pursue problems that few teams in the world have the resources to tackle. As a Principal Scientist, you will set the technical direction for your research area, influence the broader roadmap, and work closely with inference engineers to ensure your models are designed for real-time production deployment from inception. Key job responsibilities Foundation Model Scaling - Build and train large-scale multimodal foundation models for real-time speech and audio generation, from architecture design through production-scale training - Advance the scaling and efficiency of conversational modes, including the relationship between data, model size, and real time performance. - Design model architectures informed by hardware constraints and inference requirements, working with inference engineers to ensure models are servable from inception - Develop training methodologies for multimodal models that jointly process and generate speech, language, and audio in real-time streaming contexts Post-Training & Reinforcement Learning - Design and build reward models and reward functions for speech systems — capturing naturalness, fluency, conversational quality, and real-time responsiveness - Develop and apply reinforcement learning methods to shape conversational behavior — teaching models natural timing, responsiveness, and fluid interaction - Build the post-training pipeline from SFT through RL alignment, optimized for real-time multimodal outputs rather than text-only generation - Design evaluation frameworks that capture the quality dimensions unique to real-time conversation Real-Time Perception & Generation - Advance the team's capabilities in real-time perception - Work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real-time latency budgets
Flight Dynamics Engineer, Amazon Leo Flight Dynamics
US, WA, Redmond
We are searching for a talented candidate with experience in orbital mechanics, orbit determination, launch vehicle trajectories, and launch vehicle mission planning. In this position, a successful candidate would serve as a Research Scientist in support of Amazon Leo’s constellation with particular focus on Launch Vehicle support. Strong analysis skills are required to develop engineering studies of complex large-scale dynamical systems. This position requires demonstrated expertise in computational analysis automation and tool development. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities Working with the Leo GNC team, you will: • Perform spacecraft maneuver or navigation analysis in support of multi-disciplinary trades within the Amazon Leo team. • Contribute to prototype software development of flight algorithms. • Test and assess navigation software for integration into flight systems. • Assess and trouble-shoot the performance of Leo on-board GNSS hardware and software systems. • Work closely with GNC engineers to manage on-orbit performance and develop flight dynamics operations processes. • Manage engineering trades as needed for various launch vehicle mission designs. • Support Leo’s Launch Vehicle Mission Management team with technical expertise in Launch Vehicle trajectory requirements specification • Evaluate Launch Vehicle performance and compliance with mission requirements • Develop tools to support Mission Management planning for over 80 launches! • Work collaboratively with launch vehicle system technical teams About the team The Flight Dynamics team is responsible for the guidance, navigation, control and safety of the spaceflight of the Amazon Leo constellation. This team provides solutions to spaceflight challenges in constellation design, orbit selection, launch vehicle insertion requirements, navigation, trajectory design, space situational awareness, and space traffic coordination.
Applied Scientist II, Amazon Robotics
DE, Berlin
The Amazon Robotics team is seeking an experienced Applied Scientist to join our team. In this role you will apply the latest trends in research to solve real-world problems in robotics and AI. You will collaborate with a team of scientists and engineers building these applications. We holistically design, build, and deliver end-to-end robotic systems. Our team is also responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, machine learning scientists, software engineers, and hardware engineers to collaborate and deploy systems in the field. Key job responsibilities • Research, design, develop, and evaluate complex perception, motion planning, and decision making algorithms integrating across multiple disciplines and leveraging machine learning. • Create experiments and prototype implementations of new learning algorithms and prediction techniques. • Work closely with software engineering team members to drive scalable, real-time implementations. • Collaborate with machine learning and robotic controls experts to implement and deploy algorithms, such as machine learning models. • Collaborate closely with hardware engineering team members on developing systems from prototyping to production level. • Represent Amazon in academia community through publications and scientific presentations. • Work with stakeholders across hardware, science, and operations teams to iterate on systems design and implementation. About the team Watch this video to learn more about Vulcan Pick team in Amazon Robotics: https://www.amazon.science/publications/vulcan-pick-a-robotic-system-for-picking-targeted-objects-from-fabric-pods
Data Scientist, Alexa AI
IN, KA, Bengaluru
Amazon is looking for a passionate, talented, and inventive Data Scientist with machine learning background to help build industry-leading Speech and Language technology. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML). Key job responsibilities Key job responsibilities Amazon is looking for a passionate, talented, and inventive Data Scientist with machine learning background to help build industry-leading Speech and Language technology. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV). As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop data experiments, novel algorithms and data techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use of speech and language technology. You will gain hands on experience with Amazon’s heterogeneous speech, text, and structured data sources, and large-scale computing resources to accelerate advances in spoken language understanding.
Applied Scientist , Prime Video Personalization and Discovery
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities Develop AI solutions for various Prime Video Personalization systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; Effectively communicate technical and non-technical ideas with teammates and stakeholders; Stay up-to-date with advancements and the latest modeling techniques in the field; Publish your research findings in top conferences and journals. About the team Prime Video Personalization and Discovery team owns science solution to power personalized experience on various devices, from sourcing, relevance, ranking, to name a few. We work closely with the engineering and product teams to launch our solutions in production.
PhD Econ Intern, Store Economics and Science
US, WA, Seattle
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their causal inference / structural econometrics skillsets to solve real world problems. The intern will work in the area of Store Economics and Science (SEAS) and develop models to SEAS. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. About the team The Stores Economics and Science Team (SEAS) is a Stores-wide interdisciplinary team at Amazon with a "peak jumping" mission focused on disruptive innovation. The team applies science, economics, and engineering expertise to tackle the business's most critical problems, working to move from local to global optima across Amazon Stores operations. SEAS builds partnerships with organizations throughout Amazon Stores to pursue this mission, exploring frontier science while learning from the experience and perspective of others. Their approach involves testing solutions first at a small scale, then aligning more broadly to build scalable solutions that can be implemented across the organization. The team works backwards from customers using their unique scientific expertise to add value, takes on long-run and high-risk projects that business teams typically wouldn't pursue, helps teams with kickstart problems by building practical prototypes, raises the scientific bar at Amazon, and builds and shares software that makes Amazon more productive.
Applied Scientist II, International Machine Learning
IN, KA, Bengaluru
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
Applied Scientist II, Amazon Recommerce India
IN, KA, Bengaluru
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.
See more jobs
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Subscribe to our newsletter

Amazon.com | Conditions of Use | Privacy | © 1996-2026 Amazon.com, Inc. or its affiliates

Social