Image shows an autonomous surface vehicle used for bathymetric mapping and water quality monitoring
This autonomous surface vehicle used for bathymetric mapping and water quality monitoring is part of a project being pursued by researchers at the Vehicle Autonomy and Intelligence Lab (VAIL) at Indiana University Bloomington.
Courtesy of Lantao Liu

How Lantao Liu and his team are helping robots adapt to challenges

The AWS Machine Learning Research Award winner is working to develop methods and open-source libraries that can potentially benefit the artificial intelligence and robotics communities.

Lantao Liu and his team at the Vehicle Autonomy and Intelligence Lab (VAIL) at Indiana University Bloomington want to help robots get better at navigating through complex and sometimes changing environments, while also boosting their ability to assess and process data. This challenge has significant applications, particularly in the realm of environmental modeling. Liu and his team are working to develop autonomous and machine learning methods and open-source libraries that can potentially benefit both the artificial intelligence and robotics communities.

“Machine learning algorithms are increasingly being developed for robotics missions. Many critical autonomy components are data-driven, where the data comes from onboard sensors such as LiDAR, sonar, and cameras,” says Liu who also is an assistant professor within the university’s Department of Intelligent Systems Engineering in the Luddy School of Informatics, Computing, and Engineering.

Photo is of Lantao Liu, who leads the Vehicle Autonomy and Intelligence Lab at Indiana University Bloomington
Lantao Liu leads the Vehicle Autonomy and Intelligence Lab at Indiana University Bloomington.
Courtesy of Lantao Liu

“The robots typically have weak computational capacity due to their limited dimensions and payloads, yet they require online learning with data processed on the fly,” he adds. “Unfortunately, many methods for solving these tasks entail large computational costs that can be very challenging for the robots. The key challenges have been computational-theoretical due to the increased complexity of stochastic modeling, but also practical due to the synergy of integrating hardware and software systems as well as customizing algorithms on the robots.”

Liu’s 2019 Amazon Machine Learning Research Award allows VAIL to access and leverage Amazon’s cloud computing tools and services for thousands of hours, boosting their work on both machine learning and autonomous systems.

“My lab works on various decision-making problems for different types of robots including aerial, ground, and aquatic vehicles. Our objective is to develop methodologies for autonomous robots to enhance their autonomy and intelligence in environmental sensing and modeling, search and rescue, among other applications of societal importance,” explains Liu.

Environmental sensing, modeling, and monitoring

One project being pursued by VAIL researchers involves a process that maps environmental attributes of interest, such as pollution in the water or air, by collecting corresponding measurement samples from different locations so that a “distribution map" (environment model) can be reconstructed.

“This mapping mechanism is also called environmental state estimation, a learning process where the parameters of an underlying environment model must be learned using streams of incoming sampling data collected by robots,” Liu explains.

“However, the environments can be dynamic, as can the associated environmental attributes to be mapped. A drawback to using robots is that the collection of samples requires a series of sequential, ordered, sampling operations (so data may not well represent the ground-truth map), and the entire sampling process is time consuming because the samples are typically spread over different spatial locations.

Environmental sensing, modeling, and monitoring using autonomous surface vehicles

“To provide a good estimate of the state of the environment at any time, the robot information-gathering sensing must be persistent to keep up with evolving environmental dynamics,” Liu explains. “One focus of our research has been developing principles that use data-driven methods to guide robots to learn the spatio-temporal and stochastic environment model, and utilize the learned model for path planning and decision-making solutions. This, in turn, benefits future environmental exploration and exploitation for subsequent modeling and monitoring.”

The VAIL team has been developing methods and software that can accurately characterize the spatiotemporal environment by designing a non-stationary modeling framework based on a variant of Gaussian processes (GPs).

“The map will not be the same everywhere,” says Liu. “There are locations on the map that vary more rapidly than others, and we need to accurately model both rapidly and slowly changing parts. It is even more challenging when the underlying map is dynamic, such as when we’re mapping pollution dispersion.

“In addition,” he explains, “the model computation must be fast for in-the-moment decisions. However, sensing data is continuously received, and the accumulated data quickly overwhelms the robots’ computing resources. To boost the learning performance, our researchers recently developed an adaptive learning approach where the key idea is a sparse approximation mechanism that incrementally incorporates the new incoming data with a learned model supported by ‘summarized old data.”

Robotic anomaly detection

In a related project, the lab has been developing a generic robotic anomaly detection framework, motivated by field experiments.

“Commonly, robots in the field encounter sensing and behavioral anomalies,” Liu explains. “For example, one of the thrusters of the autonomous surface vehicle (ASV) might malfunction in operation, resulting in a forward motion becoming a turning motion. Or the ASV might get stuck in aquatic plants or other underwater obstacles, which are difficult to perceive using cameras or LiDARs. The inertial measurement unit (IMU) can be sensitive to external disturbances such as magnetic fields and provide drifting readings. Surrounding objects, such as a tall tree near the shore, might block the GPS signals, which leads to inaccurate localization. Sonar data can also be affected by dynamic underwater objects or environmental disturbances.

“Resilient and adaptive robotic systems require cognitive capabilities to avoid anomalies and recover and learn from failures with minimal human intervention,” Liu adds. “Equipping robots with the self-examination ability to detect sensing and behavioral faults is an essential step. The intuitive idea of anomaly detection is to develop some concept of normality and treat the observations that deviate considerably from that as anomalies.

“It is difficult, if not impossible, to handcraft a model representing the expected behaviors of different kinds of robots in various applications,” Liu explains. “The framework learns the concept of normality via deep representation learning and graph neural networks. We train the framework using contrastive learning in a semi-supervised manner that utilizes the information in a large amount of unlabeled data and, optionally, a small amount of labeled data. During the development of this framework, the AWS EC2 instances have drastically accelerated the prototyping, training, and testing processes. We are currently finalizing this framework and will open-source software.

“Hopefully,” he adds, “it will also benefit the robotics and machine learning communities at large.”

Off-road autonomy

The AWS Machine Learning Research Award also helps VAIL research off-road autonomy.

“An important challenge is the stochastic modeling of unexpected robot behaviors,” he explains. “Basically, the robots operating in real-world complex environments need to reason about the long-term results of their physical interactions with the environment, but due to the high complexity of the real world, it is generally impossible to predict future events in an accurate manner.

“For example,” says Liu, “the effect of uneven road conditions or various disturbances on the robot’s motion is hard to model (or learn from data) precisely. It is even more challenging to model the interaction between the robot and the environment, especially when the environment is dynamic. Other representative scenarios include drones flying with strong winds or submarines moving under ocean currents, where air and water flows vary significantly in both space and time.

“Thus, it is necessary for the robots to consider these epistemic uncertainties caused by a lack of precise modeling of the environment while making decisions,” he explains. “We use Markov decision process as a basis to model autonomous decision-making under uncertainty problems. The solution to these problems is a closed-loop policy that maximizes a long-term goal and satisfies the safety constraints under a probabilistic interaction model between the robot and the environment. In principle, the resulting policy can generate a sequence of motor commands that complete the task assigned by a human, given that the probabilistic model can well describe the uncertainty of the world, and the computational method can allow the robot to calculate the policy within a reasonable amount of time.

“However,” Liu continues, “many real-world problems are non-trivial, and obtaining the required probabilistic model of the world is generally impossible. Our research focuses on solving these two challenges by developing novel methods and leveraging the strong computational power of GPUs. Our current focus is on addressing the computational part of the challenge by developing two planning algorithms that allow the robot to reason about its continuous motion on complicated terrain surfaces based on the kernel method (mesh-free) and finite-element method (mesh-based). Both methods leverage a set of discrete elements to represent the value function over the continuous space. The computation over the discrete parts can be parallelized, which allows our robot to reason and compute optimal policies in real-time to navigate through complicated terrains safely and efficiently.”

VAIL researchers have been working on using sampling methods to optimize over a class of parameterized policies.

Lantao Liu and his team used AWS cloud computing services to speed up computation and analyses of robot decision-making policies in a simulated scenario.

“To do so, we first need to sample a large number of robot trajectories under the current policy, which can be computed quickly by the parallel architecture of Nvidia GPU CUDA cores,” Liu explains. “They use the gradient-based method for optimization of policy parameters: the policy is updated by computing the policy parameter gradients based on the sampled trajectories. The gradient computation and policy update involve large matrix operations, which can also be parallelized by GPUs for real-time solutions. They leverage AWS computation for this task.”

Navigable space segmentation for navigation

Liu notes that the AWS resources have also been very useful for the team’s visual autonomy research. Visual information has become increasingly important for robotic autonomy as it can provide rich information about surrounding environments, and VAIL’s visual data processing capability has been significantly improved due to the breakthrough on deep neural networks (DNNs). To develop deep approaches to process the vision perception, the team needs to develop models with complicated learning architectures, huge volumes of data, as well as various training strategies.

“A crucial capability for mobile robots to navigate in unknown environments is to construct obstacle-free space where the robot could move without collision,” Liu explains. “Roboticists have been developing methods for detecting such free space with the ray tracing of LiDAR beams to build occupancy maps in 2D or 3D space. Mapping methods with LiDAR require processing of large point cloud data, especially when a high-resolution LiDAR is used. As a much less expensive alternative, cameras have also been widely used for free space detection by leveraging DNNs to perform multi-class or binary-class segmentation of images.

Navigable space construction for robot visual navigation

“However,” he adds, “most existing DNN-based methods are built on a supervised-learning paradigm and rely on annotated datasets. The datasets usually contain a large amount of pixel-level annotated segmented images, which are prohibitively expensive and time-consuming to obtain for robotic applications in outdoor environments. To overcome limitations of fully supervised learning, we have been developing a new deep model based on variational auto-encoders. We target a representation learning-based framework to enable robots to learn navigable space segmentation in an unsupervised manner, with the aim of learning a polyline representation that compactly outlines the desired navigable space boundary. This is different from prevalent segmentation techniques which heavily rely on supervised learning strategies and typically demand immense pixel-level annotated images.

“We trained our model with the data from public datasets using GPUs,” Liu explains. “The large number of computing cores and memory space on AWS have enabled us to train our model fast and with high efficacy. This is crucial as it allows us to test and redesign models rapidly and provides great convenience to deploy the trained model to the robot systems.

“We then train our model with a small set of collected unlabeled images in real mission environments,” Liu adds. “Early testing shows that our model is able to detect navigable space in real time with high accuracy. “The computational resources provided by Amazon have greatly accelerated our design process.”

Research areas

Related content

GB, Cambridge
Our team undertakes research together with multiple organizations to advance the state-of-the-art in speech technologies. We not only work on giving Alexa, the ground-breaking service that powers Echo, her voice, but we also develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Senior Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language and Video technology. As a Senior Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech and vocal arts synthesis. Position Responsibilities: - Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. - Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. - Research and implement novel ML and statistical approaches to add value to the business. - Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR
US, WA, Seattle
The Amazon Economics Team is hiring Economist Interns. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets to solve real-world business problems. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with future job market placement. Roughly 85% of interns from previous cohorts have converted to full-time economics employment at Amazon. If you are interested, please send your CV to our mailing list at We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, NY, New York
Amazon is investing heavily in building a world-class advertising business, and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. We deliver billions of ad impressions and millions of clicks daily and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with advertised products with a high relevance bar and strict latency constraints. Sponsored Products Detail Page Blended Widgets team is chartered with building novel product recommendation experiences. We push the innovation frontiers for our hundreds of millions of customers WW to aid product discovery while helping shoppers to find relevant products easily. Our team is building differentiated recommendations that highlight specific characteristics of products (either direct attributes, inferred or machine learned), and leveraging generative AI to provide interactive shopping experiences. We are looking for a Senior Applied Scientist who can delight our customers by continually learning and inventing. Our ideal candidate is an experienced Applied Scientist who has a track-record of performing deep analysis and is passionate about applying advanced ML and statistical techniques to solve real-world, ambiguous and complex challenges to optimize and improve the product performance, and who is motivated to achieve results in a fast-paced environment. The position offers an exceptional opportunity to grow your technical and non-technical skills and make a real difference to the Amazon Advertising business. As a Senior Applied Scientist on this team, you will: * Be the technical leader in Machine Learning; lead efforts within this team and collaborate across teams * Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, perform hands-on analysis and modeling of enormous data sets to develop insights that improve shopper experiences and merchandise sales * Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. * Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. * Research new and innovative machine learning approaches. * Promote the culture of experimentation and applied science at Amazon Team video We are also open to consider the candidate in Seattle, or Palo Alto. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
US, VA, Arlington
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising products that drive discovery and sales. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Search Sourcing and Relevance team parses billions of ads to surface the best ad to show to Amazon shoppers. The team strives to understand customer intent and identify relevant ads that enable them to discover new and alternate products. This also enables sellers on Amazon to showcase their products to customers, which may, at times, be buried deeper in the search results. By showing the right ads to customers at the right time, this team improves the shopper experience, increase advertiser ROI, and improves long-term monetization. This is a talented team of machine learning scientists and software engineers working on complex solutions to understand the customer intent and present them with ads that are not only relevant to their actual shopping experience but also non-obtrusive. This area is of strategic importance to Amazon Retail and Marketplace business, driving long term growth. Key job responsibilities As a Senior Applied Scientist on this team, you will: - Be the technical leader in Machine Learning; lead efforts within this team and across other teams. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches. - Recruit Applied Scientists to the team and provide mentorship. About the team Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use reduced-form causal analysis and/or structural economic modeling methods to evaluate the impact of policies on employee outcomes, and examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
US, WA, Seattle
We are expanding our Global Risk Management & Claims team and insurance program support for Amazon’s growing risk portfolio. This role will partner with our risk managers to develop pricing models, determine rate adequacy, build underwriting and claims dashboards, estimate reserves, and provide other analytical support for financially prudent decision making. As a member of the Global Risk Management team, this role will provide actuarial support for Amazon’s worldwide operation. Key job responsibilities ● Collaborate with risk management and claims team to identify insurance gaps, propose solutions, and measure impacts insurance brings to the business ● Develop pricing mechanisms for new and existing insurance programs utilizing actuarial skills and training in innovative ways ● Build actuarial forecasts and analyses for businesses under rapid growth, including trend studies, loss distribution analysis, ILF development, and industry benchmarks ● Design actual vs expected and other metrics dashboards to assist decision makings in pricing analysis ● Create processes to monitor loss cost and trends ● Propose and implement loss prevention initiatives with impact on insurance pricing in mind ● Advise underwriting decisions with analysis on driver risk profile ● Support insurance cost budgeting activities ● Collaborate with external vendors and other internal analytics teams to extract insurance insight ● Conduct other ad hoc pricing analyses and risk modeling as needed We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | New York, NY, USA | Seattle, WA, USA
US, NY, New York
The Amazon SCOT Forecasting team seeks a Senior Applied Scientist to join our team. Our research team conducts research into the theory and application of reinforcement learning. This research is shared in top journals and conferences and has a significant impact on the field. Through our launch of several Deep RL models into production, our work also affects decision making in the real world. Members of our group have varied interests—from the mathematical foundations of reinforcement learning, to language modeling, to maintaining the performance of generative models in the face of copyrights, and more. Recent work has focused on sample efficiency of RL algorithms, treatment effect estimation, and RL agents integrating real-world constraints, as applied in supply chains. Previous publications include: - Linear Reinforcement Learning with Ball Structure Action Space - Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data - A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation - Deep Inventory Management - What are the Statistical Limits of Offline RL with Linear Function Approximation? Working collaboratively with a group of fellow scientists and engineers, you will identify complex problems and develop solutions in the RL space. We encourage collaboration across teammates and their areas of specialty, leading to creative and ambitious projects with the goal of publication and production. Key job responsibilities - Drive collaborative research and creative problem solving - Constructively critique peer research; mentor junior scientists - Create experiments and prototype implementations of new algorithms and techniques - Collaborate with engineering teams to design and implement software built on these new algorithms - Contribute to progress of the Amazon and broader research communities by producing publications We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
US, CA, Virtual Location - California
If you are interested in this position, please apply on Twitch's Career site About Us: Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate and grow their personal interests and passions. We're always live at Twitch. About the Role: As a Data Scientist, Analytics member of the Data Platform - Insights team, you'll provide data analysis and support for platform, service, and operational engineering teams at Twitch, shaping the way success is measured. Defining what questions should be asked and scaling analytics methods and tools to support our growing business. Additionally, you will help support the vision for business analytics, solutions architecture for data related business constructs, as well as tactical execution such as experiment analysis and campaign performance reporting. You are paving the way for high-quality, high-velocity decisions and will report to the Manager, Data Science. For this role, we're looking for an experienced data staff who will oversee data instrumentation, dashboard/report building, metrics reviews, inform team investments, guidance on success/failure metrics and ad-hoc analysis. You will also work with technical and non-technical staff members throughout the company, and your effort will have an impact on hundreds of partners at Twitch You Will: - Work with members of Platforms & Services to guide them towards better decision making from the available data. - Promote data knowledge and insights through managing communications with partners and other teams, collaborate with colleagues to complete data projects and ensure all parties can use the insights to further improve. - Maintain a customer-centric focus while being a domain and product expert through data, develop trust amongst peers, and ensure that the teams and programs have access to data to make decisions - Manage ambiguous problems and adapt tools to answer complicated questions. - Identify the trade-offs between speed and quality of different approaches. - Create analytical frameworks to measure team success by partnering with teams to establish success metrics, create approaches to track the data and troubleshoot errors, measure and evaluate the data to develop a common language for all colleagues to understand these metrics. - Operationalize data processes to provide partners with ad-hoc analysis, automated dashboards, and self-service reporting tools so that everyone gets a good sense of the state of the business Perks: - Medical, Dental, Vision & Disability Insurance - 401(k), Maternity & Parental Leave - Flexible PTO - Commuter Benefits - Amazon Employee Discount - Monthly Contribution & Discounts for Wellness Related Activities & Programs (e.g., gym memberships, off-site massages), -Breakfast, Lunch & Dinner Served Daily - Free Snacks & Beverages We are open to hiring candidates to work out of one of the following locations: Irvine, CA, USA | Seattle, WA, USA | Virtual Location - CA
US, WA, Bellevue
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? Have you also wondered what are different ways that the transportation assets can be used to delight the customer even more. If so, the Amazon transportation Services, Product and Science is for you . We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed Applied Scientist with strong scientific thinking, good software and statistics experience, skills to help manage projects and operations, improve metrics, and develop scalable processes and tools. The primary role of an Applied Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how we operate the middle mile network. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, machine learning , and the ability to use data and research to make changes. This role requires robust skills in research and implementation of scalable products and models . This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, CA, Los Angeles
The Alexa team is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background, to help build industry-leading Speech and Language technology. Key job responsibilities As an Applied Scientist with the Alexa team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding. About the team The Alexa team has a mission to push the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Audio Signal Processing, in order to provide the best-possible experience for our customers. We are open to hiring candidates to work out of one of the following locations: Los Angeles, CA, USA