Promotional image for 'AI lullaby,' featuring Endel Grimes
Endel, a Berlin, Germany-based provider of personalized sound environments, recently released an updated and streamlined skill for Alexa that includes "AI Lullaby", a soundscape with vocals, music, and voiceovers by Grimes (whose name is now c).
Credit: Endel

The science behind Endel's AI-powered soundscapes

Alexa Fund company releases updated and streamlined skill for Alexa that includes "AI Lullaby" soundscape with vocals, music, and voiceovers by Grimes.

(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 Amazon Alexa Fund first invested in Endel in 2018 and earlier this year participated in their $5 million Series A led by True Ventures.)   

Recently, Endel launched an updated and streamlined Endel skill for Alexa that includes the “molecular mechanisms” soundscape with original vocals, music, and voiceovers by Grimes

The company made major headlines earlier this fall when c (the artist’s new lower-case, italicized name, inspired the symbol for the speed of light) released “AI Lullaby”, a scientifically engineered sleep soundscape that’s now available on Alexa. c actually initiated the collaboration with Endel after using the app, and because of her search for sleeping aids for her young son. 

Endel co-founders Oleg Stavitsky  and Dmitry Evgrafov
From the very beginning of the company, Endel co-founders Oleg Stavitsky, CEO, (left), and Dmitry Evgrafov, sound designer, say it has been important for the company to be "rooted in science".
Credit: Vika Bogorodskaya

Endel was founded in 2018 by a team of six. It is now a 30-person operation focused on creating personal artificial intelligence-powered soundscapes that take into account an individual’s immediate conditions. It does this by assessing a person’s current state and generating an appropriate soundscape from components of its sound engine. This process was born out of scientific principles about sound’s effect on the human body and mind.

In time for the release of the updated skill for Alexa, Amazon Science contributor Tyler Hayes spoke with Endel co-founders Oleg Stavitsky (CEO) and Dmitry Evgrafov (sound designer) about how Endel uses a variety of contextual data points to play the right sounds at the right time. 

Q. What are some of the contextual signals you use to provide personalized sounds? 

Stavitsky: Circadian rhythms is one. Each person’s body has a natural, daily rhythm — an internal clock. Even if you can’t explain it exactly, you’ve likely felt the physical or mental changes happening on a daily cycle. Circadian rhythm is a sleep-wake cycle that regulates the secretion of a sleep hormone called melatonin. It repeats every 24 hours and is constantly fine-tuned through natural light levels. Scientists have been observing circadian rhythm for some time now and in 2017, the Nobel Prize was awarded to three Americans for their discovery of molecular mechanisms that control the circadian rhythm.

Related content
Built-in radar technology, deep domain adaptation for sleep stage classification, and low-latency incremental sleep tracking enable Halo Rise to deliver a seamless, no-contact way to help customers improve sleep.

We use these universal rhythms as a baseline for our sound personalization. Everyone’s circadian graph will look different depending on where they live and their sleep habits. We also use signals such as user location and time to estimate natural light levels for further personalization. In addition to the circadian rhythm, we use the ultradian rhythm, a rest-activity cycle that regulates cognitive state, mood, and energy level. It consists of roughly 110-minute energy level loops.

Evgrafov: Curated playlists full of piano or classical guitar may feel relaxing to some people at certain points throughout the day, but those ways of relaxing with music can’t adjust depending on individual factors. If one wants to effectively use these curated playlists for specific tasks, the onus falls on the listener to know the specifics of their circadian and ultradian rhythms. Instead, our app or skill creates a personalized circadian rhythm chart for each listener to target the user’s desired mood through sound. Are you in a natural energy entry slump, but still trying to focus? We adjust accordingly. 

In the case of Alexa, we use local information such as time of day, weather, and the amount of natural light exposure through which we know the circadian rhythm phase. Alexa customers must first create an account with us to utilize the skill, and can learn about our privacy policy. With our iOS app, health data also is a key signal for creating personalized sound. Using a person’s heart rate as a real-time input indicator is one essential tool for soundscape personalization. 

Related content
Scientists discuss the challenges in developing a system that can accurately estimate body fat percentage and create personalized 3D avatars of users from smartphone photos.

We can use real-time heart rate data from people wearing fitness trackers or smartwatches like Apple Watch, if they’ve agreed to allow access. With access to heart rate data, we can recognize prolonged spikes and adapt the BPM to try to bring the heart rate back to a resting level. If possible, in the future, we would be very interested in providing this kind of personalization with the new Amazon Halo

BPM isn’t the only tool we use to adjust human physiology. One study by Luciano Bernardi looked at how swelling crescendos and deflating decrescendos can affect our physiology. Bernardi found that music with a series of crescendos generally led to increased blood pressure, heart rate, and respiration; while selections with decrescendos typically had the opposite effect.

Another study looking at effects on heart rate variability when exposed to different styles of "relaxing" music found that "new age" music induced a shift in heart rate variability from higher to lower frequencies, independent of a listener’s music preference. These and other studies suggest that music can go beyond evoking emotion to impacting cardiovascular function.

Q: How has music theory informed the types of sound your Alexa skill produces? 

Evgrafov: For music composition, we first used the pentatonic scale, a set of notes ordered by pitch or frequency, because of its popularity across modern music.

Listeners may also notice that the AI-powered soundscapes are often very simple. Using less complex tones, melodies, and movement helps ease the burden on our minds. We started with simple ratios of two tonal frequencies like octaves, 2:1, or a perfect fifth, 3:2, because those are pleasing to the brain. A new model suggests music is found to be pleasing when it triggers a rhythmically consistent pattern in certain auditory neurons.

We try to reduce brain fatigue in other ways, too. While complex song structures and unique melodies may sound nice, they force our brains to work a little harder to make sense of them. This auditory experience creates alertness in listeners. Sometimes that’s the goal of the listener, but not always. It can be difficult to determine if a song uses complex or simple elements, especially without musical training. That’s why one piece of classical music might not lull listeners into a state of relaxation in the way others do.

We employ models to determine which sounds are best suited for relaxation and which are best suited for alertness and focus. Relaxation is best facilitated with mellow tones, slow chord changes, and simple structures. Our brains are constantly analyzing sound and the less detail there is, the less attention is dedicated to that task. This helps facilitate relaxation quicker and for longer periods.

The sounds that we find most calming are also linked to our biology. Research by Lee Salk dating back to the 1960s showed how infants exposed to a heart rate of 72 bpm at 85db overwhelmingly appeared happier. They cry less and put on weight easier. Studies continue to show how lower frequencies and bass can be calming.

Q. What are your plans for evolving your soundscapes, and how will science play a role in the evolution of Endel? 

Stavitsky: To effectively personalize sound through time and tone, we have based our soundscapes on the scientific principles that Dmitry has described above. To validate and take our research-based soundscapes further, we have consulted many experts. 

For example, in the initial stages of figuring out how helpful Endel could be for people, we contacted Mihaly Csikszentmihalyi, author of the book Flow. Csikszentmihalyi designed his own survey methodology while writing the book to figure out whether people were “in flow” — a focused mental state conducive to productivity. We adapted Csikszentmihalyi’s survey to be interactive inside the app. Listeners were continually asked about their feelings, state of being, and mood to improve the effectiveness of the sounds.

Sleep scientist Roy Raymann of SleepScore Labs has been instrumental in helping us create soundscapes to naturally facilitate sleep. The latest advancement includes incorporating a sleep onset period. To do this, the same jingle or sounds are played around the same time each night to trigger the body into a restful phase.

We use broadband noises, those from a wide range of frequencies, because broadband sound administration has also shown to reduce sleep onset latency. Further into the sleep cycle, Endel incorporates nature sounds such as waves to resemble human breathing because hearing breathing-like sounds can help lull people into sleep.

We also have partnered with Germany’s largest scientific institution to study the effect of colored noises on concentration in a workspace environment, and we’re working with a brain wave analysis company for a validation experiment. The study will monitor brain activity of participants listening to Endel, popular streaming music playlists, and silence, to compare the effectiveness at achieving the state of flow.

As a team, we’re rapidly evolving to incorporate the latest data to help listeners with their goals. One example: we’re currently exploring sound masking, which will lead to new ways of listening across varied environments. But other types of sounds and scenarios informed by real-time listener data are in the works, too.

Our unique ability to adapt to every individual and creative, multidisciplinary approach are our magic potion. The scientific principles and research incorporated into the platform are what make Endel so powerful.

Research areas

Related content

  • Staff writer
    April 14, 2026
    Built in collaboration with the Gray Lab at Johns Hopkins Whiting School of Engineering, the Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
  • Meiqi Sun
    April 20, 2026
    Large language models today can solve algebra, pass academic benchmarks, and generate highly structured chain-of-thought explanations. In text-only settings, they often feel startlingly intelligent — methodical, articulate, even strategic. But place those models inside an interactive environment — ask them to click buttons, scroll pages, fill out forms, and submit answers — and their behavior changes. Their careful reasoning falters. They guess where they once deduced. They adhere to templates and produce limited procedural narration: stating what they see and what they will click next, without first forming a structured plan and acting in accordance with plan. It’s as if part of their intelligence has quietly gone offline the moment the cursor appears.
    Machine learning
  • July 26, 2026
    As AI agents take on higher-stakes decisions, Lean programming language makes it possible to mathematically prove they will behave safely.
US, WA, Seattle
The Annapurna ML team is looking for a Senior Applied Scientist to work on the intersection of Artificial Intelligence and program analysis to raise the code quality bar in our state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch, TensorFlow, and JAX. Your role will involve working closely with our custom-built Machine Learning accelerators, Inferentia and Trainium, which represent the forefront of Annapurna innovation for advanced ML capabilities, and is the underpinning of Generative AI. As a Senior Applied Scientist, you'll be instrumental in designing, developing, and deploying analyzers for ML compiler stages and compiler IRs. You will architect and implement business-critical tooling, publish research, and mentor a brilliant team of experienced scientists and engineers. You will need to be technically capable, credible, and curious in your own right as a trusted scientist, innovating on behalf of our customers. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Strong experience in programming languages, compilers, program analyzers, and program synthesis engines will be a benefit in this role. A background in machine learning and AI accelerators is preferred but not required. A day in the life Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon? We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
US, WA, Seattle
Join the AWS Perimeter Protection team as a Senior Applied Scientist, where you will bring your deep ML engineering expertise to design, build, and scale AI-driven security solutions that protect AWS customers worldwide. This role is ideal for someone who has already built and shipped production ML systems at industry scale and is looking to apply that experience to high-impact security challenges. You will own the full ML lifecycle — from research and prototyping to production deployment and optimization — powering services including Web Application Firewall, DDoS Protection, Bot Management, and Infrastructure Protection. With services spanning all AWS regions and handling trillions of requests per week, you will solve complex engineering and science problems where model performance, system reliability, and low-latency inference are critical. Key job responsibilities - Design, build, and deploy production-grade ML models and systems for real-time threat detection, mitigation, and protection against evolving cyber threats at cloud scale. - Own the full ML lifecycle end-to-end — from problem formulation, data engineering, and model development through to production deployment, monitoring, and continuous improvement. - Architect and optimize ML pipelines, training infrastructure, and serving systems to meet strict latency, throughput, and reliability requirements at AWS scale. - Bridge the gap between research and production by translating novel ML approaches into robust, scalable, and maintainable systems that operate in real-time security environments. - Design and implement feature engineering workflows and large-scale data processing pipelines to support rapid experimentation and reliable model iteration. - Collaborate closely with software engineering teams to integrate ML models into distributed, low-latency security services, driving engineering decisions around model serving, infrastructure, and system design. - Analyze large-scale production data to identify patterns, anomalies, and emerging threat vectors, and translate findings into measurable improvements to detection and mitigation capabilities. - Establish and improve best practices for ML system design, model evaluation, A/B testing, and production monitoring across the team. - Mentor junior scientists and engineers, raising the bar on both scientific rigor and engineering quality.
US, WA, Seattle
The Automated Reasoning Group in the Amazon Neuron team is looking for an Applied Scientist to work on the intersection of Artificial Intelligence and program analysis to raise the code quality bar in our state-of-the-art deep learning compiler stack. This stack is designed to optimize application models across diverse domains, including Large Language and Vision, originating from leading frameworks such as PyTorch and JAX. Your role will involve working closely with our custom-built Machine Learning accelerator, Trainium, which represents the forefront of innovation for advanced ML capabilities, and is the underpinning of Generative AI. In this role as an Applied Scientist, you'll be instrumental in designing, developing, and deploying analyzers for ML compiler stages and compiler IRs. You will architect and implement business-critical tooling, publish research, and mentor a brilliant team of experienced scientists and engineers. You will need to be technically capable, credible, and curious in your own right as a trusted AWS Neuron engineer, innovating on behalf of our customers. Your responsibilities will involve tackling crucial challenges alongside a talented engineering team, contributing to leading-edge design and research in compiler technology and deep-learning systems software. Strong experience in programming languages, compilers, program analyzers, theorem provers, and program synthesis engines will be a benefit in this role. A background in machine learning and AI accelerators is preferred but not required.
IN, KA, Bengaluru
Customer addresses, Geospatial information and Road-network play a crucial role in Amazon Logistics' Delivery Planning systems. We own exciting science problems in the areas of Address Normalization, Geocode learning, Maps learning, Time estimations including route-time, delivery-time, transit-time predictions which are key inputs in delivery planning. As part of the Science team within DNT (Last Mile), you will partner closely with other scientists and engineers in a collegial environment to develop enterprise ML solutions with a clear path to business impact. The setting also gives you an opportunity to think about a complex large-scale problem for multiple years and building increasingly sophisticated solutions year over year. In the process there will be opportunity to innovate, explore SOTA and publish the research in internal and external ML conferences. Successful candidates will have deep knowledge of competing machine learning methods for large scale predictive modelling, natural language processing, semi-supervised & graph based learning. We also look for the experience to graduate prototype models to production and the communication skills to explain complex technical approaches to the stakeholders of varied technical expertise. Here is a glimpse of the problem spaces and technologies that we deal with on a regular basis: 1. De-duping and organizing addresses into hierarchy while handling noisy, inconsistent, localized and multi-lingual user inputs. We do this at the scale of millions of customers for established (US, EU) as well as emerging geographies (IN, MX). We make use of technologies like LLMs, Weak supervision, Graph-based clustering & Entity matching. We also use additional modalities such as building outlines in maps, street view images and 3P datasets, gazetteers. 2. Build a generic ML framework which leverages relationship between places to improve delivery experience by learning precise delivery locations and propagating attributes, such as business hours and safe places. 3. (Work done in sister teams) Developing systems to consume inputs from areal imagery and optimize our maps to enable efficient delivery planning. Also building models to estimate travel time, turn costs, optimal route and defect propensities. Key job responsibilities As an Applied Scientist I, your responsibility will be to deliver on a well defined but complex business problem, explore SOTA technologies including GenAI and customize the large models as suitable for the application. Your job will be to work on a end-to-end business problem from design to experimentation and implementation. There is also an opportunity to work on open ended ML directions within the space and publish the work in prestigious ML conferences. About the team Last Mile Address Intelligence (LMAI) team owns WW charter for address and location learning solutions which are crucial for efficient Last Mile delivery planning. The team works out of Hyderabad and Bangalore offices in India. LMAI is a part of the DNT Science team, which also owns problems in the space of maps learning and travel time estimations. The rest of the DNT Science team and senior leadership of Last Mile org works out of Bellevue office.
US, CA, Pasadena
We are seeking a Research Science Manager to join the SAF Lab. In this role, you will lead a team of world-class applied scientists, engineers, post-docs and interns developing the next generation of safe autonomy on highly dynamic robotic systems. You will drive technical vision, research strategy, and ensure your team's innovations translate into production systems that operate at Amazon scale. You will interface with top academic researchers at the forefront of safe autonomy, and interdisciplinary teams across Amazon working on autonomous mobile robots, mobile manipulators, and dynamically stable robots. You will bridge academic research with real-world deployable safety layers that enable robots to safely operate around humans. Key job responsibilities • Manage a team of scientists and engineers developing a universal safety layer for robotic systems, with a focus on the next generation of robots • Work with leadership to design and execute multi-year research roadmaps for the development of safe autonomy that spans all types of current and emerging robotic platforms • Lead Scientists, Engineers, post-docs and interns to realize research and development goals and provide evidence of these results in a variety of formats • Drive integration of control barrier functions with planning, perception and learning while maintaining guarantees of safe high-performance robot behavior • Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots). • Publish research findings at top-tier conferences and contribute to the broader robotics community • Build relationships with academic and industry partners to stay at the forefront of safe autonomy A day in the life 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 Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy, developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, mobile manipulators, and future platforms with dynamic stability. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning, evaluated on next-generation robots. Your work will underpin robots operating alongside people at Amazon's unprecedented scale.
US, WA, Bellevue
Transform global logistics by developing innovative technological solutions that optimize complex international shipping networks. Join a dynamic team where your advanced scientific expertise will directly impact millions of packages moving across borders, solving intricate transportation challenges through applied science. Key job responsibilities - Develop advanced deep learning and generative AI applications for sophisticated product classification systems - Lead complex modeling analyses to support strategic business decision-making and policy development - Partner with engineering and operations teams to design transformative machine learning solutions for transportation challenges - Design and document technical strategies and applied science roadmaps - Innovate and provide technological direction in international imports and exports logistics A day in the life Your workday will be an exciting blend of data exploration, innovative problem-solving, and collaborative technology development. You'll dive into complex datasets, design sophisticated machine learning models, engage with cross-functional teams, and create technological solutions that reshape international shipping capabilities. About the team We are a passionate group of scientists and engineers dedicated to building comprehensive transportation technologies. Our mission is to create highly scalable, intelligent systems that enable seamless international package movement. By pushing technological boundaries, we're reimagining global logistics through innovative applied science. 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 Learn more about our benefits here: https://www.amazon.jobs/en/internal/benefits/us-benefits-and-stock
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the limits. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As an Applied Scientist on our team, you will focus on building state-of-the-art ML models for healthcare. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform healthcare outcomes. Key job responsibilities In this role, you will: • Design and implement novel AI/ML solutions for complex healthcare challenges • Drive advancements in machine learning and data science • Balance theoretical knowledge with practical implementation • Work closely with customers and partners to understand their requirements • Navigate ambiguity and create clarity in early-stage product development • Collaborate with cross-functional teams while fostering innovation in a collaborative work environment to deliver impactful solutions • Establish best practices for ML experimentation, evaluation, development and deployment • Partner with leadership to define roadmap and strategic initiatives You’ll need a strong background in AI/ML, proven leadership skills, and the ability to translate complex concepts into actionable plans. You’ll also need to effectively translate research findings into practical solutions. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the Special Projects organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team We represent Amazon's ambitious vision to solve the world's most pressing challenges. We are exploring new approaches to enhance research practices in the healthcare space, leveraging Amazon's scale and technological expertise. We operate with the agility of a startup while backed by Amazon's resources and operational excellence. We're looking for builders who are excited about working on ambitious, undefined problems and are comfortable with ambiguity.
US, TX, Austin
Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Systems Engineer, this role is primarily responsible for the design, development and integration of communication payload and customer terminal systems. The Role: Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology at global scale. The team develops and designs the communication system for project Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in designing and developing advanced technology and solutions for communication system. This role will also be responsible developing advanced physical layer + protocol stacks systems as proof of concept and reference implementation to improve the performance and reliability of the LEO network. In particular this role will be responsible for using concepts from digital signal processing, information theory, wireless communications to develop novel solutions for achieving ultra-high performance LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. This role will also play a critical role in the integration and verification of various HW and SW sub-systems as a part of system integration and link bring-up and verification. 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.
US, CA, Sunnyvale
We are looking for a Senior Applied Scientist to help drive the research and development of real-time multimodal conversational AI. You will contribute 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 own a significant research area and contribute 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 Senior Scientist, you will drive the technical execution of your research area, contribute to the team’s roadmap, and work closely with inference engineers to ensure your models are designed for real-time production deployment. Key job responsibilities What You’ll Do Foundation Model Scaling - Help 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 models, 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 - Contribute to the state of the art on efficient architectures and training methods for conversational AI at scale 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 parts of 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 (latency sensitivity, audio quality, prosody, interaction naturalness) Real-Time Perception & Generation - Advance the team’s capabilities in real-time perception — the ability of the model to process incoming audio/speech while simultaneously generating responses - Develop techniques for natural interactive systems where the model handles concurrent input and output with human-like timing - Work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real-time latency budgets
US, WA, Bellevue
Robots are about to step out of the movies and into our homes. If you're excited about this robotics revolution, come join Amazon's Fauna Robotics. As an Applied Scientist in this org, you'll push the state-of-the-art techniques in Robotics. You'll drive technical excellence in areas such as perception, manipulation, sim2real transfer, reinforcement learning and multi-task learning, designing novel algorithms that bridge the gap between research and real-world deployment. In this role, you will integrate hands-on technical expertise with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. While staying updated with the latest advancements in robotics research, you will leverage your experience and expertise to guide the team towards the most promising direction. Collaborating with a talented group of software engineers and scientists, you will bring your ideas to fruition. Deploying robots in home environments requires you to anticipate challenges not encountered in structured settings like warehouses. Additionally, consumer-grade sensors and actuators necessitate working around their limitations, requiring out-of-the-box innovative ideas. As such, you are someone who truly enjoys thinking about problems using first principles at a holistic and systems level to address the aforementioned challenges. Key job responsibilities - Lead technical initiatives in areas such as robotics foundation models, reinforcement learning and manipulation - - Design experiments to identify the limitations of current state-of-the-art models and developing new models or techniques that can surpass these models - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus - Experienced in communicating complex technical work to a non-technical audience - The ability to work with minimal guidance, be proactive and to handle ambiguity and the challenge of quickly evolving goals 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 - Guide fellow scientists in solving complex technical challenges, from sim2real transfer to training RL policies - Mentor team members while maintaining significant hands-on contribution to technical solutions 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. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.