Social
A graphic shows a simulation of what a VR guided experience might look like, it features colored circles, a humanoid face, and other features
TRIPP is an Alexa Fund portfolio company that is exploring how virtual reality can be combined with meditation. The company's CEO and co-founder Nanea Reeves noted that the VR experiences "are deliberately psychedelic. You find yourself inside an atom or floating in space."
TRIPP

TRIPP explores the potential of virtual reality–powered meditation

Alexa Fund portfolio company’s science-led program could change how we approach mental wellness — and how we use VR.

By Staff writer
October 7, 2022
5 min read
Share

(Editor’s note: This article is the latest installment in a series by Amazon Science delving into the science behind products and services of companies in which Amazon has invested. The Alexa Fund participated in TRIPP’s $11.2M Series A extension funding round in June 2022.)

With meditation soaring in popularity and the virtual reality (VR) industry booming, Alexa Fund portfolio company TRIPP is at the confluence of two powerful trends: mental well-being and VR. TRIPP is a digital wellness program that uses VR headsets to immerse users in breathing meditation experiences.

The idea to meld meditation and VR first occurred to TRIPP CEO and co-founder Nanea Reeves in 2017 when she, a lifelong practitioner of meditation with a 15-year career in the gaming industry, was experimenting with the medium by building a solitaire game in VR.

This graphic shows a potential visualization from the VR app TRIPP, it includes a planet floating in space against a background featuring stars and bright colors
TRIPP CEO Nanea Reeves said she was inspired by the work of Giuseppe Riva, a neuropsychologist. “We wondered if we could create environments that make you go, ‘Oh, wow!’ and give you a lift,” Reeves explains.
TRIPP

“The way I felt exploring VR was even more interesting to me than the actual development,” Reeves says. Her interest developed into a deep curiosity about the psychological effects of VR, and she began searching the scientific literature.

In addition to studying VR in medical and mental health use cases, she discovered work by Giuseppe Riva, a neuropsychologist whose research has shown that VR can elicit emotions such as awe and, in turn, influence people’s well-being, agency, and perceptions of themselves.

“Inspired by Giuseppe’s research, we wondered if we could create environments that make you go, ‘Oh, wow!’ and give you a lift,” Reeves explains. That became her impetus to found TRIPP in 2017, pursuing the hypothesis that, by stimulating experiences through VR, people may gain some agency over their emotions.

Riva’s work identified several aspects of VR that made it particularly effective for evoking awe, including its immersive nature and the complexity and scale of virtual environments.

“We started leaning into that, and we started to play with scale in our designs,” says Reeves.

In 2019, TRIPP launched its award-winning wellness application, TRIPP, in VR. The application offers immersive mindfulness experiences and, to date, has delivered nearly 6 million wellness sessions. TRIPP continues to work closely with neuroscience and psychiatry advisors through PsyAssist to design and evaluate their experiences and with a host of researchers who are studying its potential uses.

A recent clinical trial suggested that TRIPP might positively influence well-being and mood in cancer patients, and ongoing projects are assessing whether TRIPP can help people in recovery from substance use disorders or anxiety and pain control, among other initiatives.

“With some of the early data coming in, it seems very promising that tools like TRIPP might have a positive impact,” Reeves says, “but we are still only at the beginning of what may be possible with VR.”

Amazon Science spoke with Reeves about the motivation for TRIPP, its roots in science, and the implications for what it can achieve with VR.

  1. Q. 

    Why take meditation virtual?
    A. 

    It’s actually a super native case for VR, because the immersive quality allows you to unplug. You can’t look at your phone by nature of the immersion. I was blown away by how I felt moving in and out of VR — the feeling of respite.

    A lot of the early VR experiences simulated environments that made you terrified, like walking a plank or standing at the edge of a cliff. And it occurred to me: if we could scare people so easily, could we trigger other emotional changes that are beneficial?

    Our approach is to use immersion to drive a deeper connection to self-contemplative thoughts. You’re immersed in this psychologically safe container that is designed to capture your awareness, to allow you to be present and connect to self. With VR, it’s not just an audio file guiding you toward connecting with your breath. We can show you your breath, like star crystals coming in and out of your mouth.

    Some of the early offerings thought VR was really only for gaming. When we first got TRIPP on Oculus, they didn’t know where to put us because they didn’t have a relaxation genre then. We were pretty buried in the store. But we started to get a little bit of traction, and we soon had surprisingly regular usage over time. VR has this problem where you go in, you go, “Wow, that was amazing!” and then it's in your closet. You never pick it up again. When we were designing the product, we knew we had to build for repeat usage. So, we built it procedurally, and now we can update it from the server and add new content regularly.

  2. Q. 

    How is science leading the design and development of TRIPP?
    A. 

    I started by looking at the research investigating VR’s effect on the brain, largely by neuroscientists and behavioral therapists exploring mental health and medical use cases for VR. Most of them focused on simulation, like simulated exposure therapy. But I wondered if, instead of simulating something, could we stimulate responses? I took that question to the researchers working on VR and its effects on mental well-being. We started digging into the research and then, based on the research data, designed VR experiences intended to trigger states of wonder and awe. We tried them out on people and captured some physiological data like heart rate and EEG to see if the experiences were directionally achieving the effect we wanted.

    We stay very grounded in research-driven design choices. We’re a bit edgy. Rather than mimicking natural environments, the experiences are deliberately psychedelic. You find yourself inside an atom or floating in space. First, we tried things like a waterfall or a beach, but when we tested them, they didn’t feel good. I found some research describing how sensory dissonance in synthetic natural environments can create a stress response. Your brain goes, “Well, it looks like a beach, but I don’t smell the ocean. So what’s wrong with this environment?” We realized we had to create environments for which you have no mental model. So we said, “OK, let’s put you in a cosmic flotation tank, whatever that means, because no one knows what space smells like.”

    We’re also starting an enterprise program to support employee wellness and team-building with a hybrid workforce, but also with a focus on using the metaverse for driving ideation and innovation. Can we use these otherworldly environments to spark creativity?
    Nanea Reeves

    After we launched, we started collecting data on mood and emotional state in the live user sessions via self-reported surveys that are based on what they use in mental health research. We bookended our sessions with users, basically asking “How do you feel?” before and after each session. We did things like randomizing the order of the response selections to control against position bias. Now, we have data collected from close to 6 million sessions that we can slice and dice by the time of day and, for a percentage of our audience who opted in, age, gender, and location. It’s become a meaningful body of data that we can share with researchers once it has been de-identified. We also work with researchers who want to study TRIPP and its potential applications. It’s been fascinating to work with that community.

    One example is this group in Germany working with cancer patients undergoing surgery, looking at the effects of TRIPP sessions on their quality of life, well-being, and mood. This study was published in Nature’s Scientific Reports. Participation was good, and patients who used TRIPP had lower heart rates and breathing rates after their sessions. They had improved scores on their mood and emotion surveys after the sessions too. What the study shows us is that VR-based relaxation is a viable option and may be beneficial for cancer patients undergoing surgery. Of course, we don’t make any therapeutic claims until we have a tremendous amount of evidence to support those claims.

    You can find other published data and scientific reports examining TRIPP’s effects on adolescents and its use in palliative care. And we’re working with researchers who are looking at whether TRIPP could help people experiencing addiction and rehab, chemotherapy, severe mental illness, anxiety, and pain. Researchers are also using TRIPP to ease anxiety and help people prepare for ketamine-assisted therapy, so they get the most out of their treatment. I feel very motivated to see how we can help and participate in these research initiatives.

  3. Q. 

    How might TRIPP change how we use VR, beyond its medical applications?
    A. 

    Earlier this year, we acquired EvolVR, which holds live group meditations. Several hundred avatars attend the most popular conversations. People really open up. Skip Rizzo, PhD, a renowned researcher in the field and an advisor to TRIPP, did a pilot study and found that people suffering from PTSD were more open and honest with an avatar. We want to further investigate this as there’s something about the “digital veil” of VR where you don’t feel the judgment of being seen by other people. I think this is a wonderful use case for the metaverse.

    We’re also starting an enterprise program to support employee wellness and team-building with a hybrid workforce, but also with a focus on using the metaverse for driving ideation and innovation. Can we use these otherworldly environments to spark creativity?

    It’s also fascinating to think about the gig economy that could emerge from the metaverse — similar to what first emerged from mobile computing. For example, I met a woman at one of our live group meditation events in the metaverse who’s earning a living as one of the top world-builders in Altspace VR. She’s a mother of five who has never worked in tech before. She’s now a programmer, building these virtual worlds.

    We’re only beginning to explore the potential of VR. It’s like the early days of mobile. Initially, no one thought we’d want to play games or even text message on our cell phones. Now, for the past decade, we have been walking around with our heads down staring at these phone screens nonstop. What kind of innovation will happen in a future when XR glasses are ubiquitous and allow us to experience the world around us in an entirely new and adaptive way?

Tags
About the Author
Staff writer

Related content

Work with us

See more jobs See more jobs
Applied Scientist, Pricing Science
US, WA, Seattle
Pricing is one of the most consequential decisions Amazon makes — and the science behind it needs to be causally rigorous, not just predictive. The P2 Optimization Science (P2OS) team builds the machine learning systems that power Amazon's pricing decisions at scale: demand lift models, customer lifetime value frameworks, and the experimentation infrastructure that validates whether our pricing changes actually work. We're hiring an Applied Scientist to own causal inference at the intersection of ML and pricing experimentation. This role exists because our team has identified a real gap: the methodological bridge between econometric analysis (owned by our economists) and production-scale ML pipelines (owned by our engineers) needs a practitioner who lives in both worlds. You'll build CATE estimation models, design analysis workflows for pricing weblabs, and develop the reusable causal ML infrastructure that the broader team — including non-ML scientists — can rely on. This is not a research role. The bias here is toward shipping production-quality causal pipelines with real downstream business impact. You'll measure success by what changes in LTV estimates, what pricing errors your models help avoid, and whether the economists on your team can actually use what you build. If you're a scientist who wants to work on hard causal identification problems in a high-stakes production environment — and who finds satisfaction in making rigorous methods accessible to a broader team — this role is for you. Key job responsibilities * Build causal ML pipelines for pricing — Design, train, evaluate, and deploy end-to-end causal estimation models for pricing use cases. * Own the science on heterogeneous treatment effects — Be the team SME on causal ML methodology: identification strategies, model selection, evaluation standards, and the tradeoffs between econometric and ML approaches to causal estimation. * Support pricing experiment analysis — Contribute causal analysis methodology to pricing weblab and A/B test post-analysis; build reusable tooling that economists can use without requiring ML expertise * Connect model outputs to business outcomes — Define, before writing code, what business metric each model moves; deliver model evaluation reports framed around pricing errors avoided and LTV estimate changes. * Evaluate and adopt novel techniques — Assess applicability of emerging causal inference methods (synthetic DiD, generalized random forests, causal representation learning) to Amazon's pricing context; write internal methodology proposals for adoption * Write internal documentation and methodology papers — Produce at least one internal write-up per half that connects a causal ML technique to a concrete pricing use case; make pipelines extensible and well-documented so other scientists can build on them. * Collaborate across disciplines — Partner closely with the Sr. Economist on identification strategy and causal assumptions; work with SDE and DE partners on production deployment; align with PMs on experiment design requirements A day in the life As an Applied Scientist on the P2OS team, your work directly shapes the prices customers see on hundreds of millions of Amazon products. In a given workweek, you might: * Investigate an optimization anomaly in simulation and trace it back to a model input gap or an unmodeled market dynamic * Design an offline evaluation framework to benchmark competing optimization approaches before committing to online testing * Collaborate with Sr. Economists on the identification strategy for the model you're building for a pricing lab * Present a science proposal for incorporating a new competitiveness or inventory signal into an optimization system * Work cross-team with the experimentation platform team on randomization design. * Develop and write up a novel scientific finding — preparing a paper or technical report for submission to a top-tier venue such as KDD, NeurIPS, or the ACM Conference on Economics and Computation
Data Scientist, Traffic Quality
IN, KA, Bengaluru
Amazon Ads is a multi-billion dollar global business that delivers advertising experiences across Amazon's owned-and-operated properties (including Prime Video, Twitch, Fire TV, and Amazon.com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity. Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising. We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency. Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods. We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience. Key job responsibilities As a Data Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection by applying advanced statistical techniques and machine learning. You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined. - Apply new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights. - Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Mentor and develop junior scientists on the team. About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences. AdKDD 2023.](https://assets.amazon.science/b7/42/03be071743d5a57cb1656e6caa34/scaling-generative-pre-training-for-user-ad-activity-sequences.pdf) 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)](https://assets.amazon.science/75/2f/3b7106b143f38f7f4d2806388ace/real-time-detection-of-robotic-traffic-in-online-advertising.pdf) 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop](https://openreview.net/forum?id=wIIJlmr1Dsk)
Applied Scientist, Fauna
US, CA, Sunnyvale
We are seeking an Applied Scientist to focus on Robot Navigation. In this role, you'll research and develop advanced navigation systems that enable robots to move reliably and safely through complex, dynamic environments. You'll work across a broad spectrum of navigation approaches—from classical methods to learning-based techniques and foundation models—to build robust solutions for autonomous robot navigation. Key job responsibilities - Develop and implement robust navigation systems that enable reliable autonomous operation in complex, dynamic indoor environments with static and dynamic obstacles - Build simulation-based and on-device evaluation frameworks with comprehensive benchmarks and metrics for systematic comparison of navigation methods - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world navigation performance - Collaborate with world model, manipulation, and other teams to ensure seamless integration of navigation capabilities into the full robot system - Stay current with the latest advances in robot navigation, spatial reasoning, and related fields, and apply relevant findings to improve system performance - Mentor fellow scientists and engineers while maintaining strong individual technical contributions About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products.
Applied Scientist II, Text-to-Speech on Device team
PL, Gdansk
Have you ever wondered how we give voice to devices — even when they're offline? The Text-to-Speech on Device team at Amazon builds AI-powered voice models that run locally on hardware with limited resources, serving customers across Alexa, automotive, and accessibility experiences for visually impaired users. We sit at the intersection of speech generation, generative AI, and on-device machine learning, and we're looking for a curious, collaborative Applied Scientist to help us push what's possible. In this role, you will research and develop production-ready speech generation models optimized for constrained environments. You will work across the full model lifecycle — from early experimentation and prototyping through to integration on real devices. If you're excited about solving hard scientific problems that directly improve how millions of people interact with technology, we'd love to hear from you. Key job responsibilities - Design and develop end-to-end machine learning models for on-device speech generation, from early research and experimentation through production-ready deployment. - Research and apply advanced techniques in generative AI, model compression, and knowledge distillation to deliver high-quality voice models within tight hardware constraints. - Propose and validate novel scientific approaches by authoring detailed technical specifications and contributing to peer-reviewed publications when appropriate. - Evaluate model performance rigorously, identify improvement opportunities, and iterate on training and inference pipelines to optimize quality and efficiency. - Collaborate with science and engineering teams across cloud and device platforms to bring speech generation capabilities from research prototypes to integrated product experiences. About the team The Text-to-Speech on Device team builds low-footprint AI models for speech generation that run locally on devices such as Android and FireOS platforms. Our models require significantly less computation than cloud-hosted alternatives, enabling offline voice experiences for Alexa, automotive partners, and accessibility solutions. We work closely with device engineering teams and cloud-based speech science teams to deliver the best possible experience for our customers. Our focus in the coming years is expanding the range of voices and languages we support while continuing to improve naturalness and efficiency on constrained hardware.
Applied Scientist, Navigation
US, CA, Sunnyvale
Amazon is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments. At Amazon, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started. As a Sr. Scientist in Robot Navigation, you will be at the forefront of this transformation — architecting and delivering navigation systems that are intelligent, safe, and scalable. You will bring deep expertise in learning-based planning and control, a strong understanding of foundation models and their application to embodied agents, and as well as have in-depth understanding of control-theoretic approaches such as model predictive control (MPC)-based trajectory planning. You will develop navigation solutions that seamlessly blend data-driven intelligence with principled control-theoretic guarantees. Our vision is bold: to build navigation systems that allow robots to move fluidly and safely through dynamic environments — understanding context, anticipating change, and adapting in real time. You will lead research that bridges the gap between cutting-edge academic advances and production grade deployment, collaborating with world-class teams pushing the boundaries of robotic autonomy, manipulation, and human-robot interaction. Join us in building the next generation of intelligent navigation systems that will define the future of autonomous robotics at scale. Key job responsibilities - Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding - Lead research initiatives in computer vision, sensor fusion and 3D perception - Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities - Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment - Mentor junior scientists and engineers; contribute to a culture of technical excellence - Define and track key metrics to measure perception system performance in real-world environments - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a group is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.
Applied Scientist II, Translation Services
IN, TS, Hyderabad
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Hyderabad office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
Applied Scientist II, CMT
IN, KA, Bengaluru
As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities Thought leader on the team and help set team directions Research multiple problem domains, suggest various approaches to try and be as hands-on as needed while providing more junior scientists with critical mentorship Collaborate with engineers to come up with the right LLD and HLD to solve key business problems Strong emphasis on communication via writing, internal and external talks, and being able to align with multiple stakeholders A day in the life As an Applied scientist II, a typical day will involve aligning with key product, engineering and business stakeholders ; advising junior scientists on the work they are doing ; reading current research papers and staying up-to-date on AI research ; diving deep as needed to improve CMT models and addressing stakeholders from the science perspective ; writing python code
Applied Scientist, CMT
IN, KA, Bengaluru
As a member of the CMT team, you'll play a key role in the evolution of our Competitive Monitoring systems to solve significantly complex and interesting technical challenges in machine learning, large language models in production, and recommender systems to name a few. The team's work directly impacts customer experience at a worldwide scale. Key job responsibilities 1. Research the problem domain and come up with various approaches to solve the problem. 2. Be willing to experiment quickly and fail fast. 3. Collaborate with engineers to come up with the right end to end solution to the business problems. 4. Ideate on future roadmap for science in CMT 5. Be willing to roll up your sleeves and learn core topics outside applied science, for example ML engineering A day in the life A typical day might involve (a) working on ideas for improving models around product similarity or price recommendations, (b) working closely with other scientists and our ML engineers to ensure that the best models are in production, (c) writing good maintainable code that can be reused and reproduced, (d) sharing your work across CMT and beyond via technical writings and presentations
Applied Scientist-II, Amazon Music - Search Science, Amazon Music – Search Science
IN, KA, Bengaluru
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. The Amazon Music Search Science team is seeking an innovative and driven Applied Scientist to join our engineering and science hub in Bangalore. You will work alongside a world-class team of machine learning experts to break new ground in understanding user intent, classifying complex audio and musical forms, and creating next-generation interactive search experiences that help users find the exact music, podcasts, and audio content they are in the mood for. In this role, you will own the design, development, and deployment of end-to-end machine learning systems. You will balance execution on core search and discovery priorities—such as improving retrieval accuracy, latency, and relevance for millions of daily queries—while laying the foundational modeling capabilities for broader semantic understanding and advanced conversational search experiences across mobile, web, and voice-forward devices (like Alexa and Echo). Key job responsibilities - Core Search & Execution: Collaborate with scientists, software engineers, and product managers to define, frame, and solve complex business and ranking problems as machine learning, information retrieval, or optimization tasks. - Advanced AI & Modeling: Design, build, train, and evaluate production-grade ML models using classical machine learning, deep learning, Large Language Models (LLMs), and Agentic AI techniques to scale music discovery and intent resolution. - End-to-End Production Ownership: Take algorithms from research ideation to production deployment. Build scalable data pipelines, efficient model-serving systems, and robust offline/online evaluation frameworks. - Experimentation & Iteration: Design and analyze large-scale A/B experiments across millions of customers to measure impact on search relevance, engagement, and customer satisfaction, refining models for continuous improvement. - Forward-Looking Innovation: Research and implement novel statistical and machine learning approaches, exploring multi-modal understanding, rich content semantics, and advanced retrieval mechanisms that extend beyond traditional search boundaries. - Technical Communication: Communicate findings, architectural decisions, and technical roadmaps clearly to both technical peers and executive stakeholders, authoring robust design documents and contributing to team standards. Basic Qualifications - PhD, or Master’s degree and 4+ years of relevant experience in Computer Science, Computer Engineering, Machine Learning, Statistics, or a related quantitative field. - 3+ years of hands-on experience building machine learning models or algorithms for business applications and deploying them into production. - Strong programming skills in Python, Java, C++, or related languages, with a solid foundation in data structures, algorithms, and object-oriented design. - Experience in one or more of the following areas: Information Retrieval, Natural Language Processing (NLP), Recommender Systems, Deep Learning, or Numerical Optimization. - Demonstrated ability to work effectively with cross-functional teams in a fast-paced environment. Preferred Qualifications - Experience with large-scale distributed computing frameworks and big data systems (e.g., Spark, Hadoop, AWS infrastructure). - Experience building search ranking, query understanding, or semantic retrieval systems for high-scale consumer applications. - Familiarity with modern foundation models, LLMs, fine-tuning techniques, and efficient inference optimization for production services. - Track record of peer-reviewed publications or patents at top-tier machine learning/AI conferences (e.g., NeurIPS, KDD, ACL, SIGIR, ICML). - Experience in designing, executing, and evaluating rigorous online A/B experiments.
Senior Applied Scientist, Amazon Music - Search Science, Amazon Music – Search Science
IN, KA, Bengaluru
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. The Amazon Music Search Science team is looking for an execution-focused Senior Applied Scientist to spearhead core scientific initiatives within our Bangalore hub. In this leadership-by-example role, you will define and execute the applied science roadmap for search and content discovery systems, directly impacting millions of customers worldwide. You will operate at the exciting intersection of large-scale search infrastructure, applied machine learning, and foundation models. You will drive immediate, high-impact business deliverables in music search relevance, personalization, and retrieval performance, while simultaneously architecting the long-term technological vision that expands our search ecosystem toward deeper semantic intelligence, agentic workflows, and cross-domain audio understanding. Key job responsibilities - Strategic Roadmap & Architecture: Define and execute the technical and scientific roadmap for music search systems, making critical architectural decisions that balance short-term feature delivery with long-term scalability, maintainability, and extensibility. - Pioneering Applied Science: Lead the design and implementation of state-of-the-art machine learning solutions leveraging deep learning, LLMs, and agentic workflows to solve complex search, ranking, and intent-matching challenges. - Cross-Functional Leadership: Partner closely with product management, engineering leaders, and peer teams to harmonize technical direction and deliver synchronized customer experiences. - Technical Excellence & Mentorship: Drive engineering and scientific excellence across the team by conducting rigorous design reviews, establishing modeling best practices, setting high bars for artifact delivery, and mentoring junior/mid-level scientists. - Experimentation & Scaling: Establish robust scientific processes for large-scale data analysis, offline model validation, and online A/B experimentation, ensuring high statistical rigor and measurable business impact across millions of active listeners. - Stakeholder Influence & Writing: Author strategic whitepapers, and executive-level documentation. Communicate complex technical options and trade-offs to senior leadership to drive informed decision-making. Basic Qualifications - PhD, or Master’s degree and 6+ years of applied research and industrial machine learning experience in Computer Science, Machine Learning, or a related field. - 3+ years of specialized experience designing, building, and scaling machine learning models for core production business applications (e.g., Search, Recommendation Systems, or Large-Scale Information Retrieval). - Expert programming proficiency in Python, Java, C++, or related languages, combined with deep familiarity with neural network architectures and deep learning frameworks. - Proven track record of owning end-to-end technical deliverables from problem formulation and model architecture to production deployment and performance tuning. - Demonstrated leadership in mentoring technical talent and driving engineering/scientific best practices. Preferred Qualifications - Deep expertise in search retrieval, query understanding, ranking algorithms, and large-scale vector search/embedding systems. - Experience building applied science solutions on top of foundation models, large language models (LLMs), or multi-modal architectures. - Experience with large-scale distributed training and inference optimization on cloud infrastructure (AWS). - A strong publication record or patent portfolio in top-tier peer-reviewed venues (e.g., NeurIPS, SIGIR, KDD, ACL, ICML). - Experience designing and interpreting complex online experimentation frameworks for consumer-facing recommendation or search products.
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