Neural encoding enables more-efficient recovery of lost audio packets

By leveraging neural vocoding, Amazon Chime SDK’s new deep-redundancy (DRED) technology can reconstruct long sequences of lost packets with little bandwidth overhead.

Packet loss is a big problem for real-time voice communication over the Internet. Everyone has been in the situation where the network is becoming unreliable and enough packets are getting lost that it's hard — or impossible — to make out what the other person is saying.

One way to fight packet loss is through redundancy, in which each new packet includes information about prior packets. But existing redundancy schemes either have limited scope — carrying information only about the immediately preceding packet, for instance — or scale inefficiently.

The Deep REDundancy (DRED) technology from the Amazon Chime SDK team significantly improves quality and intelligibility under packet loss by efficiently transmitting large amounts of redundant information. Our approach leverages the ability of neural vocoders to reconstruct informationally rich speech signals from informationally sparse frequency spectrum snapshots, and we use a neural encoder to compress those snapshots still further. With this approach, we are able to load a single packet with information about as many as 50 prior packets (one second of speech) with minimal increase in bandwidth.

We describe our approach in a paper that we will present at this year’s ICASSP.

Redundant audio

All modern codecs (coder/decoders) have so-called packet-loss-concealment (PLC) algorithms that attempt to guess the content of lost packets. Those algorithms work fine for infrequent, short losses, as they can extrapolate phonemes to fill in gaps of a few tens of milliseconds. However, they cannot (and certainly should not try to) predict the next phoneme or word from the conversation. To deal with significantly degraded networks, we need more than just PLC.

Related content
Combining classic signal processing with deep learning makes method efficient enough to run on a phone.

One option is the 25-year-old spec for REDundant audio data (often referred to as just RED). Despite its age, RED is still in use today and is one of the few ways of transmitting redundant data for WebRTC, a popular open-source framework for real-time communication over the Web. RED has the advantage of being flexible and simple to use, but it is not very efficient. Transmitting two copies of the audio requires ... twice the bitrate.

The Opus audio codec — which is the default codec for WebRTC — introduced a more efficient scheme for redundancy called low-bit-rate redundancy (LBRR). With LBRR, each new audio packet can include a copy of the previous packet, encoded at a lower bit rate. That has the advantage of lowering the bit rate overhead. Also, because the scheme is deeply integrated into Opus, it can be simpler to use than RED.

That being said, the Opus LBRR is limited to just one frame of redundancy, so it cannot do much in the case of a long burst of lost packets. RED does not have that limitation, but transmitting a large number of copies would be impractical due to the overhead. There is always the risk that the extra redundancy will end up causing congestion and more losses.

LBRR and PLC.png
With every new voice packet (blue), Opus’s low-bit-rate-redundancy (LBRR) mechanism includes a compressed copy of the previous packet (green). When three consecutive packets are lost (red x’s), two of them are unrecoverable, and a packet-loss-concealment (PLC) algorithm must fill in the gaps.

Deep REDundancy (DRED)

In the past few years, we have seen neural speech codecs that can produce good quality speech at only a fraction of the bit rate required by traditional speech codecs — typically less than three kilobits per second (3 kb/s). That was unthinkable just a few years ago. But for most real-time-communication applications, neural codecs aren't that useful, because just the packet headers required by the IP/UDP/RTP protocols take up 16 kb/s.

However, for the purpose of transmitting a large amount of redundancy, a neural speech codec can be very useful, and we propose a Deep REDundancy codec that has been specifically designed for that purpose. It has a different set of constraints than a regular speech codec:

  • The redundancy in different packets needs to be independent (that's why we call it redundancy in the first place). However, within each packet, we can use as much prediction and other redundancy elimination as we like since IP packets are all-or-nothing (no corrupted packets).
  • We want to encode meaningful acoustic features rather than abstract (latent) ones to avoid having to standardize more than needed and to leave room for future technology improvements.
  • There is a large degree of overlap between consecutive redundancy packets. The encoder should leverage this overlap and should not need to encode each redundancy packet from scratch. The encoding complexity should remain constant even as we increase the amount of redundancy.
  • Since short bursts are more common than long ones, the redundancy decoder should be able to decode the most recent audio quickly but may take longer to decode older signals.
  • The Opus decoder has to be able to switch between decoding DRED, PLC, LBRR, and regular packets at any time.

Neural vocoders

Let's take a brief detour and discuss neural vocoders. A vocoder is an algorithm that takes in acoustic features that describe the spectrum of a speech signal over a short span of time and generates the corresponding (continuous) speech signal. Vocoders can be used in text-to-speech, where acoustic features are generated from text, and for speech compression, where the encoder transmits acoustic features, and a vocoder generates speech from the features.

Related content
A text-to-speech system, which converts written text into synthesized speech, is what allows Alexa to respond verbally to requests or commands...

Vocoders have been around since the ’70s, but none had ever achieved acceptable speech quality — until neural vocoders like WaveNet came about and changed everything. WaveNet itself was all but impossible to implement in real time (even on a GPU), but it led to lower-complexity neural vocoders, like the LPCNet vocoder we're using here.

Like many (but not all) neural vocoders, LPCNet is autoregressive, in that it produces the audio samples that best fit the previous samples — whether the previous samples are real speech or speech synthesized by LPCNet itself. As we will see below, that property can be very useful.

DRED architecture

The vocoder’s inputs — the acoustic features — don't describe the full speech waveform, but they do describe how the speech sounds to the human ear. That makes them lightweight and predictable and thus ideal for transmitting large amounts of redundancy.

The idea behind DRED is to compress the features as much as possible while ensuring that the recovered speech is still intelligible. When multiple packets go missing, we wait for the first packet to arrive and decode the features it contains. We then send those features to a vocoder — in our case, LPCNet — which re-synthesizes the missing speech for us from the point where the loss occurred. Once the "hole" is filled, we resume with Opus decoding as usual.

Combining the constraints listed earlier leads to the encoder architecture depicted below, which enables efficient encoding of highly redundant acoustic features — so that extended holes can be filled at the decoder.

Codec.png
Every 20 milliseconds, the DRED encoder encodes the last 40 milliseconds of speech. The decoder works backward, as the most recently transmitted audio is usually the most important.

The DRED encoder works as follows. Every 20 milliseconds (ms), it produces a new vector that contains information about the last 40 ms of speech. Given this overlap, we need only half of the vectors to reconstruct the complete speech. To avoid our redundancy’s being itself redundant, in a given 20 ms packet, we include only every other redundancy coding vector, so the redundancy encoded in a given packet covers nonoverlapping segments of the past speech. In terms of the figure above, the signal can be recovered from just the odd/purple blocks or just the even/blue blocks.

Related content
The team’s non-real-time system is the top performer, while its real-time system finishes third overall and second among real-time systems — despite using only 4% of a CPU core.

The degree of redundancy is determined by the number of past chunks included in each packet; each chunk included in the redundancy coding corresponds to 40 ms of speech that can be recovered. Furthermore, rather than representing each chunk independently, the encoder takes advantage of the correlation between successive chunks and extracts a sort of interchunk difference to encode.

For decoding, to be able to synthesize the whole sequence, all we need is a starting point. But rather than decoding forward in time, as would be intuitive, we choose an initial state that corresponds to the most recent chunk; from there, we decode going backward in time. That means we can get quickly to the most recent audio, which is more likely to be useful. It also means that we can transmit as much — or as little — redundancy as we want just by choosing how many chunks to include in a packet.

Rate-distortion-optimized variational autoencoder

Now let's get into the details of how we minimize the bit rate to code our redundancy. Here we turn to a widely used method in the video coding world, rate distortion optimization (RDO), which means trying to simultaneously reduce the bit rate and the distortion we cause to the speech. In a regular autoencoder, we train an encoder to find a simple — typically, low-dimensional — vector representation of an input that can then be decoded back to something close to the original.

In our rate-distortion-optimized variational autoencoder (RDO-VAE), instead of imposing a limit on the dimensionality of the representation, we directly limit the number of bits required to code that representation. We can estimate the actual rate (in bits) required to code the latent representation, assuming entropy coding of a quantized Laplace distribution. As a result, not only do we automatically optimize the feature representation, but the training process automatically discards any useless dimensions by setting them to zero. We don't need to manually choose the number of dimensions.

Moreover, by varying the rate-distortion trade-off, we can train a rate-controllable quantizer. That allows us to use better quality for the most recent speech (which is more likely to be used) and a lower quality for older speech that would be used only for a long burst of loss. In the end, we use an average bit rate of around 500 bits/second (0.5 kb/s) and still have enough information to reconstruct intelligible speech.

Once we include DRED, this is what the packet loss scenario described above would look like:

DRED vs. LBRR.png
With LBRR, each new packet (blue) includes a compressed copy of the previous packet (green); with DRED, it includes highly compressed versions of up to 50 prior packets (red). In this case, DRED's redundancy is set at 140 ms (seven packets).

Although it is illustrated for just 70 milliseconds of redundancy, we scale this up to one full second of redundancy contained in each 20-millisecond packet. That's 50 copies of the information being sent, on the assumption that at least one will make it to its destination and enable reconstruction of the original speech.

Revisiting packet loss concealment

So what happens when we lose a packet and don't have any DRED data for it? We still need to play out something — and ideally not zeros. In that case, we can just guess. Over a short period of time, we can still predict acoustic features reasonably well and then ask LPCNet to fill in the missing audio based on those features. That is essentially what PLC does, and doing it with a neural vocoder like LPCNet works better than using traditional PLC algorithms like the one that's currently integrated into Opus. In fact, our neural PLC algorithm recently placed second in the Interspeech 2022 Audio Deep Packet Loss Concealment Challenge.

Results

How much does DRED improve speech quality and intelligibility under lossy network conditions? Let's start with a clip compressed with Opus wideband at 24 kb/s, plus 16 kb/s of LBRR redundancy (40 kb/s total). This is what we get without loss:

Clean audio

To show what happens in lossy conditions, let's use a particularly difficult — but real — loss sequence taken from the PLC Challenge. If we use the standard Opus redundancy (LBRR) and PLC, the resulting audio is missing large chunks that just cannot be filled:

Lossy audio with LBRR and PLC

If we add our DRED coding with one full second of redundancy included in each packet, at a cost of about 32 kb/s, the missing speech can be entirely recovered:

Lossy audio with DRED
Results.png
Overall results of DRED's evaluation on the full dataset for the original PLC Challenge, using mean opinion score (MOS).

The example above is based on just one speech sequence, but we evaluated DRED on the full dataset for the original PLC Challenge, using mean opinion score (MOS) to aggregate the judgments of human reviewers. The results show that DRED alone (no LBRR) can reduce the impact of packet loss by about half even compared to our previous neural PLC. Also interesting is the fact that LBRR still provides a benefit even when DRED is used. With both LBRR and DRED, the impact of packet loss becomes very small, with just a 0.1 MOS degradation compared to the original, uncompressed speech.

This work is only one example of how Amazon is contributing to improving Opus. Our open-source neural PLC and DRED implementations are available on this development branch, and we welcome feedback and outside collaboration. We are also engaging with the IETF with the goal of updating the Opus standard in a fully compatible way. Our two Internet drafts (draft 1 | draft 2) offer more details on what we are proposing.

Research areas

Related content

US, MA, Westborough
Amazon is looking for talented Postdoctoral Scientists to join our Fulfillment Technology and Robotics team for a one-year, full-time research position. The Innovation Lab in BOS27 is a physical space in which new ideas can be explored, hands-on. The Lab provides easier access to tools and equipment our inventors need while also incubating critical technologies necessary for future robotic products. The Lab is intended to not only develop new technologies that can be used in future Fulfillment, Technology, and Robotics products but additionally promote deeper technical collaboration with universities from around the world. The Lab’s research efforts are focused on highly autonomous systems inclusive of robotic manipulation of packages and ASINs, multi-robot systems utilizing vertical space, Amazon integrated gantries, advancements in perception, and collaborative robotics. These five areas of research represent an impactful set of technical capabilities that when realized at a world class level will unlock our desire for a highly automated and adaptable fulfillment supply chain. As a Postdoctoral Scientist you will be developing a coordinated multi-agent system to achieve optimized trajectories under realistic constraints. The project will explore the utility of state-of-the-art methods to solve multi-agent, multi-objective optimization problems with stochastic time and location constraints. The project is motivated by a new technology being developed in the Innovation Lab to introduce efficiencies in the last-mile delivery systems. Key job responsibilities In this role you will: * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent new techniques in your area(s) of expertise.
IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team!
GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problems. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
US, CA, Palo Alto
Amazon’s Advertising Technology team builds the technology infrastructure and ad serving systems to manage billions of advertising queries every day. The result is better quality advertising for publishers and more relevant ads for customers. In this organization you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world's leading companies. Amazon Publisher Services (APS) helps publishers of all sizes and on all channels better monetize their content through effective advertising. APS unites publishers with advertisers across devices and media channels. We work with Amazon teams across the globe to solve complex problems for our customers. The end results are Amazon products that let publishers focus on what they do best - publishing. The APS Publisher Products Engineering team is responsible for building cloud-based advertising technology services that help Web, Mobile, Streaming TV broadcasters and Audio publishers grow their business. The engineering team focuses on unlocking our ad tech on the most impactful Desktop, mobile and Connected TV devices in the home, bringing real-time capabilities to this medium for the first time. As a successful Data Scientist in our team, · You are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, and can credibly interface between technical teams and business stakeholders. You will collaborate directly with product managers, BIEs and our data infra team. · You will analyze large amounts of business data, automate and scale the analysis, and develop metrics (e.g., user recognition, ROAS, Share of Wallet) that will enable us to continually measure the impact of our initiatives and refine the product strategy. · Your analytical abilities, business understanding, and technical aptitude will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future. · You will have direct exposure to senior leadership as we communicate results and provide scientific guidance to the business. Major responsibilities include: · Utilizing code (Apache, Spark, Python, R, Scala, etc.) for analyzing data and building statistical models to solve specific business problems. · Collaborate with product, BIEs, software developers, and business leaders to define product requirements and provide analytical support · Build customer-facing reporting to provide insights and metrics which track system performance · Influence the product strategy directly through your analytical insights · Communicating verbally and in writing to business customers and leadership team with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
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! In Prime Video READI, our mission is to automate infrastructure scaling and operational readiness. We are growing a team specialized in time series modeling, forecasting, and release safety. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment. As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
US, WA, Bellevue
mmPROS Surface Research Science seeks an exceptional Applied Scientist with expertise in optimization and machine learning to optimize Amazon's middle mile transportation network, the backbone of its logistics operations. Amazon's middle mile transportation network utilizes a fleet of semi-trucks, trains, and airplanes to transport millions of packages and other freight between warehouses, vendor facilities, and customers, on time and at low cost. The Surface Research Science team delivers innovation, models, algorithms, and other scientific solutions to efficiently plan and operate the middle mile surface (truck and rail) transportation network. The team focuses on large-scale problems in vehicle route planning, capacity procurement, network design, forecasting, and equipment re-balancing. Your role will be to build innovative optimization and machine learning models to improve driver routing and procurement efficiency. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. You will operate as part of a team of innovative, experienced scientists working on optimization and machine learning. You will work in close collaboration with partners across product, engineering, business intelligence, and operations. Key job responsibilities - Design and develop optimization and machine learning models to inform our hardest planning decisions. - Implement models and algorithms in Amazon's production software. - Lead and partner with product, engineering, and operations teams to drive modeling and technical design for complex business problems. - Lead complex modeling and data analyses to aid management in making key business decisions and set new policies. - Write documentation for scientific and business audiences. About the team This role is part of mmPROS Surface Research Science. Our mission is to build the most efficient and optimal transportation network on the planet, using our science and technology as our biggest advantage. We leverage technologies in optimization, operations research, and machine learning to grow our businesses and solve Amazon's unique logistical challenges. Scientists in the team work in close collaboration with each other and with partners across product, software engineering, business intelligence, and operations. They regularly interact with software engineering teams and business leadership.
US, WA, Seattle
Success in any organization begins with its people and having a comprehensive understanding of our workforce and how we best utilize their unique skills and experience is paramount to our future success.. Come join the team that owns the technology behind AWS People Planning products, services, and metrics. We leverage technology to improve the experience of AWS Executives, HR/Recruiting/Finance leaders, and internal AWS planning partners. A Sr. Data Scientist in the AWS Workforce Planning team, will partner with Software Engineers, Data Engineers and other Scientists, TPMs, Product Managers and Senior Management to help create world-class solutions. We're looking for people who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history. You will leverage your knowledge in machine learning, advanced analytics, metrics, reporting, and analytic tooling/languages to analyze and translate the data into meaningful insights. You will have end-to-end ownership of operational and technical aspects of the insights you are building for the business, and will play an integral role in strategic decision-making. Further, you will build solutions leveraging advanced analytics that enable stakeholders to manage the business and make effective decisions, partner with internal teams to identify process and system improvement opportunities. As a tech expert, you will be an advocate for compelling user experiences and will demonstrate the value of automation and data-driven planning tools in the People Experience and Technology space. Key job responsibilities * Engineering execution - drive crisp and timely execution of milestones, consider and advise on key design and technology trade-offs with engineering teams * Priority management - manage diverse requests and dependencies from teams * Process improvements – define, implement and continuously improve delivery and operational efficiency * Stakeholder management – interface with and influence your stakeholders, balancing business needs vs. technical constraints and driving clarity in ambiguous situations * Operational Excellence – monitor metrics and program health, anticipate and clear blockers, manage escalations To be successful on this journey, you love having high standards for yourself and everyone you work with, and always look for opportunities to make our services better.
US, WA, Bellevue
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Senior Applied Scientist to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. An Applied Scientist will be a tech lead for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical expertise and a passion for developing science-driven solutions in a fast-paced environment. The ideal candidate will have a solid understanding of state of the art NLP, Generative AI, LLM fine-tuning, alignment, prompt engineering, benchmarking solutions, or CV and Multi-modal models, e.g., Vision Language Models (VLM), zero-shot, few-shot, and semi-supervised learning paradigms, with the ability to apply these technologies to diverse business challenges. You will leverage your deep technical knowledge, a strong foundation in machine learning and AI, and hands-on experience in building large-scale distributed systems to deliver reliable, scalable, and high-performance products. In addition to your technical expertise, you must have excellent communication skills and the ability to influence and collaborate effectively with key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll lead the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the hiring group About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.
US, WA, Bellevue
Why this job is awesome? This is SUPER high-visibility work: Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site. MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers. We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process. You will have a chance to develop the state-of-art machine learning, including deep learning and reinforcement learning models, to build targeting, recommendation, and optimization services to impact millions of Amazon customers. - Do you want to join an innovative team of scientists and engineers who use machine learning and statistical techniques to deliver the best delivery experience on every Amazon-owned site? - Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms 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 DEX AI team. Key job responsibilities - Research and implement machine learning techniques to create scalable and effective models in Delivery Experience (DEX) systems - Solve business problems and identify business opportunities to provide the best delivery experience on all Amazon-owned sites. - Design and develop highly innovative machine learning and deep learning models for big data. - Build state-of-art ranking and recommendations models and apply to Amazon search engine. - Analyze and understand large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation