Line Haul at DS.jpeg
Amazon Transportation Services' Middle Mile team “has made longstanding contributions to operations research and analytics for decades, and their impact has been widely noted and recognized,” said Erica Klampfl, 2021 INFORMS Prize committee chair.
Credit: AboutAmazon.com

How Amazon's Middle Mile team helps packages make the journey to your doorstep

The Middle Mile team manages complexity and scale in making routing decisions across the company’s expansive transportation network.

Amazon Transportation Services’ Middle Mile team develops routing solutions to move customer orders from its vendors and fulfillment centers to its network of sortation centers, air facilities, and delivery stations in the most efficient way possible.

Watch Amazon's trucks line up outside a facility

Over the past five years, the team has played a critical role in helping Amazon deliver on increasingly ambitious goals — from two-day and one-day deliveries for Prime customers, to one-hour delivery windows for services like Prime Now.

Recently, INFORMS, the leading international association for operations research and analytics professionals, recognized these achievements by awarding Amazon the 2021 INFORMS Prize. The award recognizes the effective integration of operations research and analytics into organizational decision making.

The INFORMS prize logo is shown atop the Amazon logo
The INFORMS prize "is awarded for effective integration of advanced analytics and operations research/management sciences (OR/MS) in an organization."
INFORMS.org

“Amazon has made longstanding contributions to operations research and analytics for decades, and their impact has been widely noted and recognized,” said Erica Klampfl, 2021 INFORMS Prize committee chair. “Amazon is truly deserving of this prestigious prize, and the entire O.R. and analytics community joins INFORMS in thanking them for all they have done and continue to do.”

Over the past five years, the team has doubled down on scientific innovation and operations research to move millions of packages globally through Amazon’s transportation network. The INFORMS award serves as a reminder not just of the work the Middle Mile team has done at Amazon, but also how far they have come.

An extremely complex problem

Given the high number of variables involved in arriving at optimal routing decisions, complexity is a constant for Amazon’s Middle Mile team.

For every customer order, Amazon’s routing algorithms must determine the best path through the network to move the product between suppliers, fulfillment centers, sorting facilities, and delivery stations, to quickly, safely, and cost-effectively reach customers.

They must evaluate the merits of each transportation option — surface, rail, air, or maritime — and determine the most effective route.  The algorithms also determine an optimal or near-optimal route to send the order to a facility where it can be sorted and handed off for delivery. Finally, all schedules have to be designed in a way that optimizes for safety and complies with government regulations such as rest breaks, hours of service, and other requirements.

Our trucking network alone presents us with over ten octovigintillion possible routing solutions.
Tim Jacobs

“To give you an idea of the scale and complexity we’re managing, our trucking network alone presents us with over 1088 or ten octovigintillion — possible routing solutions,” says Tim Jacobs, director of Middle Mile Research Science and Optimization. “This is an especially large number, when you consider that there are 1082 atoms in the visible universe.”

And that’s just for the trucking network.

When a product is ordered on the Amazon Store, there are several ways it can make its way from a fulfillment center to the customer’s residence.

There’s the (relatively) straightforward approach: The product is sent from a fulfillment center to a sortation center and to a delivery station, at which point it is placed on a vehicle for delivery to the customer’s residence.

There are also more involved scenarios, such as when customers place time-sensitive orders for items stored in geographically distant fulfillment centers. In these cases, the products are often delivered using a combination of Amazon’s air cargo network along with the surface network to meet the customer’s delivery timelines.

When Jacobs joined Amazon in 2016, the majority of the company’s loads were carried by a relatively small number of large third-party carriers that managed the truck assignments and routings. Since then, the Middle Mile team has helped to develop new ways to manage its transportation network, including by routing a growing number of medium and small carriers using Amazon’s own technology and algorithms, enabling more efficient management and visibility of the transportation network, which in turn helps Amazon get packages to customers faster and more efficiently.

That effort began, in part, by expanding the team.

In the beginning: Improving Amazon’s surface operations

In 2016, Mauricio Resende was among just a few scientists in Amazon’s Middle Mile team — a number that has since grown significantly.

Prior to Amazon, Resende worked as a scientist at AT&T Labs focused on combinatorial optimization. At its essence, combinatorial optimization involves using mathematical methods to identify the best decisions for a problem from a large set of candidate solutions.

“In 2016, Amazon’s surface routing decisions were made using a basic local search algorithm,” Resende says. “Loads were allocated in advance. The process was largely iterative, and we drove small improvements to the algorithm week over week.”

Tim Jacobs, director of Middle Mile Research Science and Optimization; Mauricio Resende, principal research scientist; and Nilay Noyan, principal research scientist
Named among others in Amazon's 2021 INFORMS Prize were (from left) Tim Jacobs, director of Middle Mile Research Science and Optimization, Mauricio Resende, principal research scientist, and Nilay Noyan, principal research scientist.

Crucially, in order to automate routing decisions, the algorithms and systems needed to account for differing constraints and inputs that have a profound impact on routing decisions, such as the nuances of different regulatory agencies in each country.

The system also needed to understand the storage and throughput constraints of each facility by considering factors like operating hours or whether parking slips might be required. So, the team worked to model and eliminate those system blind spots.

“We developed more advanced data structures and algorithmic techniques to account for these constraints as we designed routing schedules,” says Resende.

Resende provides the example of a sequence evaluator designed by Amazon’s Middle Mile research team. The evaluator was designed to help find the most effective routing solution for a pre-determined objective function, such as cost, or number of trips with empty loads.

The evaluator computed the cost for a presented route. It kept working through possible changes to the route until a near-optimal route was found. This solution was then perturbed — routes were deliberately eliminated and new deliveries were fed into the algorithm. The task was then repeated. In this manner, the algorithm progressed toward an iteratively better solution.

Through methods such as these, Resende and his fellow researchers drove a significant reduction in surface transportation costs.

When you are working with such a large universe of possibilities, you have to be incredibly efficient in how you formulate the problem.
Mauricio Resende

“When you are working with such a large universe of possibilities, you have to be incredibly efficient in how you formulate the problem,” says Resende. “You then have to be efficient in designing algorithms to solve that formulation of the problem.”

The Middle Mile team also faced situations where it had to route goods that hadn’t been accounted for in the demand forecasts that are an input to its routing plans. While future demand can be predicted, there are still many unknowns at the planning stage. A good example is spikes in demand for new products, or products that become unexpectedly popular.

To cope with demand variability, the Middle Mile team developed a truckload supply load board with dynamic pricing. The load board, powered by a number of machine learning algorithms coupled with mathematical optimization models, allowed Amazon to expand its delivery network by accessing the available capacity of pre-screened carriers operating in a geographical area or lane.

The load board dynamically sets prices for loads that are currently available. Carriers can review available loads simultaneously. Interested carriers can then accept the load at the offered price in real-time. This arrangement also helps carriers optimize the efficiency of their drivers’ schedules.

As Amazon drove improvements to its surface network, the Middle Mile team also leveraged scientific innovation to design routing solutions for its air cargo service, which has expanded rapidly since launching in 2016.

Developing algorithms to manage Amazon’s fleet of contracted airline partners

Nilay Noyan joined the company as a principal research scientist in September 2019. Prior to Amazon, Noyan was a professor of industrial engineering at Sabanci University in Istanbul.

Broadly speaking, the air routing problems are similar to those for surface networks. However, there are completely different constraints associated with airlines.
Nilay Noyan

“Broadly speaking, the air routing problems are similar to those for surface networks,” says Noyan. “However, there are completely different constraints associated with airlines.”

These include regulatory constraints, lead times for procuring aircraft, the impact of fluctuating fuel prices, and resources required to manage airline contracts. Flight schedule designs also need to ensure that there is sufficient time for routine line maintenance, airplane refueling, and the loading and unloading of packages.

Arrival and departure times must be aligned with available capacity and resources to ensure packages are processed on time. To further complicate matters, airline schedules have to be aligned with those of the surface network so there are trucks waiting on the ground to carry packages to the next destination.

Over the past four years, the Middle Mile Planning Research and Optimization Science team has developed and implemented more than a dozen optimization and machine learning models to build and operate the air transport network. These tools help the team arrive at the most optimal decisions in areas such as flight schedule design, fuel management, package flow planning, maintenance planning, and disruption recovery.

Noyan says machine learning also plays an important role in helping the Middle Mile team solve for problems that are inherently stochastic or unpredictable in nature.

Amazon Prime Air Boeing 767
Over the past four years, the Middle Mile Planning Research and Optimization Science team has developed and implemented more than a dozen optimization and machine learning models to build and operate the air transport network.
Chad Slattery

“Deviations from the execution plans are unavoidable in case of unexpected disruption events due to weather, unscheduled maintenance, and crew-related delays,” says Noyan. “Machine-learning-based prediction methods help us react to these unexpected situations, and adapt quickly so that we can meet our delivery promises to customers.”

In addition to helping Amazon adapt to unpredictable events, Jacobs sees machine learning playing an increasingly important role in helping Amazon more effectively unify the worlds of surface, air, rail and maritime networks for both network design and day of operations.  

“At Amazon, we work back backwards from the customer,” he says. “We don’t think of each mode of transport separately, as is common in the industry. Instead, we are continually working to combine these areas effectively, so that the way we plan and the way we operate the network are consistent.”

Related content

GB, London
"Are you a MS or PhD student interested in the fields of Computer Science or Operational Research? Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products? If this describes you, come join our research teams at Amazon. " Key job responsibilities The candidate will be responsible for the design and implementation of optimization algorithms. The candidate will translate high-level business problems into mathematical ones. Then, they will design and implement optimization algorithms to solve them. The candidate will be responsible also for the analysis and design of KPIs and input data quality. About the team ATS stands for Amazon Transportation Service, we are the middle-mile planners: we carry the packages from the warehouses to the cities in a limited amount of time to enable the “Amazon experience”. As the core research team, we grow with ATS business to support decision making in an increasingly complex ecosystem of a data-driven supply chain and e-commerce giant. We take pride in our algorithmic solutions: We schedule more than 1 million trucks with Amazon shipments annually; our algorithms are key to reducing CO2 emissions, protecting sites from being overwhelmed during peak days, and ensuring a smile on Amazon’s customer lips. We do not shy away from responsibility. Our mathematical algorithms provide confidence in leadership to invest in programs of several hundreds millions euros every year. Above all, we are having fun solving real-world problems, in real-world speed, while failing & learning along the way. We employ the most sophisticated tools: We use modular algorithmic designs in the domain of combinatorial optimization, solving complicated generalizations of core OR problems with the right level of decomposition, employing parallelization and approximation algorithms. We use deep learning, bandits, and reinforcement learning to put data into the loop of decision making. We like to learn new techniques to surprise business stakeholders by making possible what they cannot anticipate. For this reason, we work closely with Amazon scholars and experts from Academic institutions. We are open to hiring candidates to work out of one of the following locations: London, GBR
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a highly-skilled Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and push the boundaries of efficient inference for Generative Artificial Intelligence (GenAI) models. As a Senior Applied Scientist, you will play a critical role in driving the development of GenAI technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Design and execute experiments to evaluate the performance of different decoding algorithms and models, and iterate quickly to improve results - Develop deep learning models for compression, system optimization, and inference - Collaborate with cross-functional teams of engineers and scientists to identify and solve complex problems in GenAI - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA | New York, NY, USA
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, and basic familiarity with Python or R, is necessary. Experience with SQL is 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 apply econometric methods to support business decisions, collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Collaborate with business and science colleagues to understand the business problem and collect relevant data. Provide statistically rigorous analysis of data that contributes to business decision-making. Effectively communicate your results to colleagues and business leaders. A day in the life Meet with colleagues to discuss how the business currently works. Discuss ways in which the customer experience could be improved, and what data you'd need to test your hypotheses. Meet with data and business intelligence engineers to build an efficient data pipeline using SQL, spark and other big data tools. Propose and execute a plan to analyze your data, working closely with your econ colleagues. Use Amazon's development tools, coding your estimators in Python or R. Draft your findings for an internal knowledge sharing session. Iterate to improve your work and communicate your final results in a business document. About the team We are a team of four economists that works within the delivery experience org. Our goal is to improve the delivery experience for our customers while reducing costs. This mission puts us in a unique position to influence both the front end customer experience and the supply chain that ultimately places constraints on that experience. This means we often work with and influence teams outside of our own organization. As a result, we have the privilege of working with a diverse group of experts, including those in supply chain, operations, capacity management, and user experience. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
ES, B, Barcelona
"Are you a MS or PhD student interested in the fields of Computer Science or Operational Research? Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products? If this describes you, come join our research teams at Amazon. " Key job responsibilities The candidate will be responsible for the design and implementation of optimization algorithms. The candidate will translate high-level business problems into mathematical ones. Then, they will design and implement optimization algorithms to solve them. The candidate will be responsible also for the analysis and design of KPIs and input data quality. About the team ATS stands for Amazon Transportation Service, we are the middle-mile planners: we carry the packages from the warehouses to the cities in a limited amount of time to enable the “Amazon experience”. As the core research team, we grow with ATS business to support decision making in an increasingly complex ecosystem of a data-driven supply chain and e-commerce giant. We take pride in our algorithmic solutions: We schedule more than 1 million trucks with Amazon shipments annually; our algorithms are key to reducing CO2 emissions, protecting sites from being overwhelmed during peak days, and ensuring a smile on Amazon’s customer lips. We do not shy away from responsibility. Our mathematical algorithms provide confidence in leadership to invest in programs of several hundreds millions euros every year. Above all, we are having fun solving real-world problems, in real-world speed, while failing & learning along the way. We employ the most sophisticated tools: We use modular algorithmic designs in the domain of combinatorial optimization, solving complicated generalizations of core OR problems with the right level of decomposition, employing parallelization and approximation algorithms. We use deep learning, bandits, and reinforcement learning to put data into the loop of decision making. We like to learn new techniques to surprise business stakeholders by making possible what they cannot anticipate. For this reason, we work closely with Amazon scholars and experts from Academic institutions. We are open to hiring candidates to work out of one of the following locations: Barcelona, B, ESP
IN, TN, Chennai
DESCRIPTION The Digital Acceleration (DA) team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms for solving Digital businesses problems. Key job responsibilities - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues BASIC QUALIFICATIONS - Experience building machine learning models or developing algorithms for business application - PhD, or a Master's degree and experience in CS, CE, ML or related field - Knowledge of programming languages such as C/C++, Python, Java or Perl - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. PREFERRED QUALIFICATIONS - 3+ years of building machine learning models or developing algorithms for business application experience - Have publications at top-tier peer-reviewed conferences or journals - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment We are open to hiring candidates to work out of one of the following locations: Chennai, TN, IND
US, VA, Arlington
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. The Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities The primary responsibilities of this role are to: - Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries - Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them - Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solution About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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 AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. 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 AWS, 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 (gender 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. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Atlanta, GA, USA | Austin, TX, USA | Houston, TX, USA | New York, NJ, USA | New York, NY, USA | San Francisco, CA, USA | Santa Clara, CA, USA | Seattle, WA, USA
US, WA, Seattle
Prime Video offers customers a vast collection of movies, series, and sports—all available to watch on hundreds of compatible devices. U.S. Prime members can also subscribe to 100+ channels including Max, discovery+, Paramount+ with SHOWTIME, BET+, MGM+, ViX+, PBS KIDS, NBA League Pass, MLB.TV, and STARZ with no extra apps to download, and no cable required. Prime Video is just one of the savings, convenience, and entertainment benefits included in a Prime membership. More than 200 million Prime members in 25 countries around the world enjoy access to Amazon’s enormous selection, exceptional value, and fast delivery. 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! Key job responsibilities As a Data Scientist at Amazon Prime Video, you will work with massive customer datasets, provide guidance to product teams on metrics of success, and influence feature launch decisions through statistical analysis of the outcomes of A/B experiments. You will develop machine learning models to facilitate understanding of customer's streaming behavior and build predictive models to inform personalization and ranking systems. You will work closely other scientists, economists and engineers to research new ways to improve operational efficiency of deployed models and metrics. A successful candidate will have a strong proven expertise in statistical modeling, machine learning, and experiment design, along with a solid practical understanding of strength and weakness of various scientific approaches. They have excellent communication skills, and can effectively communicate complex technical concepts with a range of technical and non-technical audience. They will be agile and capable of adapting to a fast-paced environment. They have an excellent track-record on delivering impactful projects, simplifying their approaches where necessary. A successful data scientist will own end-to-end team goals, operates with autonomy and strive to meet key deliverables in a timely manner, and with high quality. About the team Prime Video discovery science is a central team which defines customer and business success metrics, models, heuristics and econometric frameworks. The team develops, owns and operates a suite of data science and machine learning models that feed into online systems that are responsible for personalization and search relevance. The team is responsible for Prime Video’s experimentation practice and continuously innovates and upskills teams across the organization on science best practices. The team values diversity, collaboration and learning, and is excited to welcome a new member whose passion and creativity will help the team continue innovating and enhancing customer experience. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, NJ, Newark
Employer: Audible, Inc. Title: Data Scientist II Location: 1 Washington Street, Newark, NJ, 07102 Duties: Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL/ETL queries. Import processes through various company specific interfaces for accessing RedShift, and S3/edX storage systems. Build relationships with stakeholders and counterparts, and communicate model outputs, observations, and key performance indicators (KPIs) to the management to develop sustainable and consumable products. Explore and analyze data by inspecting univariate distributions and multivariate interactions, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build production-ready models using statistical modeling, mathematical modeling, econometric modeling, machine learning algorithms, network modeling, social network modeling, natural language processing, or genetic algorithms. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. Position reports into Newark, NJ office; however, telecommuting from a home office may be allowed. Requirements: Requires a Master’s in Statistics, Computer Science, Data Science, Machine Learning, Applied Math, Operations Research, Economics, or a related field plus two (2) years of Data Scientist or other occupation/position/job title with research or work experience related to data processing and predictive Machine Learning modeling at scale. Experience may be gained concurrently and must include: Two (2) years in each of the following: - Building statistical models and machine learning models using large datasets from multiple resources - Non-linear models including Neural Nets or Deep Learning, and Gradient Boosting - Applying specialized modelling software including Python, R, SAS, MATLAB, or Stata. One (1) year in the following: - Using database technologies including SQL or ETL. Alternatively, will accept a Bachelor's and five (5) years of experience. Multiple positions. Apply online: www.amazon.jobs Job Code: ADBL135. We are open to hiring candidates to work out of one of the following locations: Newark, NJ, USA
US, WA, Bellevue
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Scientist with the AGI team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of audio technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in AGI in audio domain. About the team Our team has a mission to push the envelope of AGI in audio domain, 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: Bellevue, WA, USA | Boston, MA, USA
DE, BE, Berlin
Are you fascinated by revolutionizing Alexa user experience with LLM? The Artificial General Intelligence (AGI) team is looking for an Applied Scientist with background in Large Language Model, Natural Language Process, Machine/Deep learning. You will be at the heart of the Alexa LLM transition working with a team of applied and research scientists to bring classic Alexa features and beyond into LLM empowered Alexa. You will interact in a cross-functional capacity with science, product and engineering leaders. Key job responsibilities * Work on core LLM technologies (supervised fine tuning, prompt optimization, etc.) to enable Alexa use cases * Research and develop novel metrics and algorithms for LLM evaluation * Communicating effectively with leadership team as well as with colleagues from science, engineering and business backgrounds. We are open to hiring candidates to work out of one of the following locations: Berlin, BE, DEU