Line art of silicon chips developed by Annapurna Labs since its acquisition by Amazon in 2015.  Line art includes mentions of Graviton, Inferentia, and Trainium chips, along with AWS Nitro system.
Amazon's acquisition of Annapurna Labs in 2015 has led to, among other advancements, the development of five generations of the AWS Nitro system, three generations of Arm-based Graviton processors, as well as AWS Trainium and AWS Inferentia chips that are optimized for machine learning training and inference. These chips and systems were discussed at the AWS Silicon Innovation Day event on August 3. The event included a talk by Nafea Bshara, AWS vice president and distinguished engineer, on silicon innovation emerging from Annapurna Labs.

How silicon innovation became the ‘secret sauce’ behind AWS’s success

Nafea Bshara, AWS vice president and distinguished engineer, discusses Annapurna Lab’s path to silicon success; Annapurna co-founder was a featured speaker at AWS Silicon Innovation Day virtual event.

Nafea Bshara, Amazon Web Services vice president and distinguished engineer, and the co-founder of Annapurna Labs, an Israeli-based chipmaker that Amazon acquired in 2015, maintains a low profile, as does his friend and Annapurna co-founder, Hrvoye (Billy) Bilic.

Nafea Bshara headshot image
Nafea Bshara, AWS vice president and distinguished engineer.

Each executive’s LinkedIn profile is sparse, in fact, Bilic’s is out of date.

“We hardly do any interviews; our philosophy is to let our products do the talking,” explains Bshara.

Those products, and silicon innovations, have done a lot of talking since 2015, as the acquisition has led to, among other advancements, the development of five generations of the AWS Nitro System, three generations (1, 2, 3) of custom-designed, Arm-based Graviton processors that support data-intensive workloads, as well as AWS Trainium, and AWS Inferentia chips optimized for machine learning training and inference.

Some observers have described the silicon that emerges from Annapurna Labs in the U.S. and Israel as AWS’s “secret sauce”.

Nafea’s silicon journey began at Technion University in Israel, where he earned bachelor’s and master’s degrees in computer engineering, and where he first met Hrvoye. The two then went on to work for Israel-based Galileo, a company that made chips for networking switches, and controllers for networking routers. Galileo was acquired by U.S. semiconductor manufacturer Marvell in 2000, where Bshara and Bilic would work for a decade before deciding to venture out on their own.

“We had developed at least 50 different chips together,” Bshara explained, “so we had a track record and a first-hand understanding of customer needs, and the market dynamics. We could see that some market segments were being underserved, and with the support from our spouses, Lana and Liat, and our funding friends Avigdor [Willenz] and Manuel [Alba], we started Annapurna Labs.”

That was mid-2011, and three and half years later Amazon acquired the company. The two friends have continued their journey at Amazon, where their team’s work has spoken for itself.

Last year, industry analyst David Vellante praised AWS’s “revolution in system architecture.”

“Much in the same way that AWS defined the cloud operating model last decade, we believe it is once again leading in future systems. The secret sauce underpinning these innovations is specialized designs… We believe these moves position AWS to accommodate a diversity of workloads that span cloud, data center as well as the near and far edge.”

Annapurna’s work was highlighted during the AWS Silicon Innovation Day virtual event on August 3. In fact, Nafea was a featured speaker in the event. The Silicon Innovation Day broadcast, which highlighted AWS silicon innovations, included a keynote from David Brown, vice president, Amazon EC2; a talk about the history of AWS silicon innovation from James Hamilton, Amazon senior vice president and distinguished engineer who holds more than 200 patents in 22 countries in server and datacenter infrastructure, database, and cloud computing; and a fireside chat on the Nitro System with Anthony Liguori, AWS vice president and distinguished engineer, and Jeff Barr, AWS vice president and chief evangelist.

In advance of the silicon-innovation event, Amazon Science connected with Bshara to discuss the history of Annapurna, how the company and the industry have evolved in the past decade, and what the future portends.

  1. Q. 

    You co-founded Annapurna Labs just over 11 years ago. Why Annapurna?


     I co-founded the company with my longtime partner, Billy, and with an amazing set of engineers and leaders who believed in the mission. We started Annapurna Labs because we looked at the way the chip industry was investing in infrastructure and data centers; it was minuscule at that time because everybody was going after the gold rush of mobile phones, smartphones, and tablets.

    We believed the industry was over indexing on investment for mobile, and under investing in the data center. The data center market was underserved. That, combined with the fact that there was increasing disappointment with the ineffective and non-productive method of developing chips, especially when compared with software development. The productivity of software developers had improved significantly in the past 25 years, while the productivity of chip developers hadn’t improved much since the ‘90s. In assessing the opportunity, we saw a data-center market that was being underserved, and an opportunity to redefine chip development with greater productivity, and with a better business model. Those factors contributed to us starting Annapurna Labs.

  2. Q. 

    How has the chip industry evolved in the past 11 years?


    The chip industry realized, a bit late, but nevertheless realized that productivity and time to market needed to be addressed. While Annapurna has been a pioneer in advancing productivity and time to market, many others are following in our footsteps and transitioning to a building-blocks-centric development mindset, similar to how the software industry moved toward object-oriented, and service-oriented software design.

    Chip companies have now transitioned to what we refer to as an intellectual property-oriented, or IP-oriented, correct-by-design approach. Secondly, the chip industry has adopted the cloud. Cloud adoption has led to an explosion of compute power for building chips. Using the cloud, we are able to use compute in a ‘bursty’ way and in parallel. We and our chip-industry colleagues couldn’t deliver the silicon we do today without the cloud. This has led to the creation of a healthy market where chip companies have realized they don’t need to build everything in house, in much the same way software companies have realized they can buy libraries from open source or other library providers. The industry has matured to the point where now there is a healthy business model around buying building blocks, or IPs, from providers like Arm, Synopsys, Alphawave, or Cadence.

  3. Q. 

    Annapurna Labs was named after one of the tallest peaks in the Himalayas that’s regarded as one of the most dangerous mountains to climb. What's been the tallest peak you've had to climb?


    I’m up in the cloud, I don’t need to climb anything [laughing]. Yes, Billy and I picked the name Annapurna Labs for a couple of reasons. First, Billy and I originally planned to climb Annapurna before we started the company. But then we got excited about the idea, acquired funding, and suddenly time was of the essence, so we put our climbing plans on hold and started the company. We called it Annapurna because at that time – and it’s true even today – there is a high barrier to entry in starting a chip company. The challenge is steep, and the risk is high, so it’s just like climbing Annapurna. We also believed that we wanted to reach a point above the clouds where you could see things very clearly, and without clutter. That’s always been a mantra for us as a company: Avoid the clutter, and look far into the future to understand what the customer really needs versus getting distracted by the day-to-day noise.

  4. Q. 

    What are the unique challenges you face in designing chips for ML training and inference versus more general CPU designs?


    First, I would want to emphasize what challenge we didn’t have to worry about: with the strong foundation, methodologies, and engineering muscle we built delivering multiple generations of Nitro, we had confidence in our ability to execute on building the chips and manufacturing them at high volume, and high quality. So that was a major thing we didn’t need to worry about. Designing for machine learning is one the most challenging, but also the most rewarding tasks I've had the pleasure to participate in. There is an insatiable demand for machine learning right now, so anyone with a good product won’t have any issues finding customer demand. The demand is there, but there are a couple of challenges.

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    The first is that customers want ‘just works’ solutions because they have enough challenges to work on the science side. So they are looking for a frictionless migration from the incumbent, let's say GPU-based machine learning, to AWS Trainium or AWS Inferentia. Our biggest challenge is to hide all the complexity so it’s what we refer to internally as boring to migrate. We don’t want our customers, the scientists and researchers, to have to think about moving from one piece of hardware to another. This is a challenge because the incumbent GPUs, specifically NVIDIA, have done a very good job developing broadly adopted technologies. The customer shouldn’t see or experience any of the hard work we’ve done in developing our chips; what the customer should experience is that it’s transparent and frictionless to transition to Inferentia and Trainium. That’s a hefty task and one of our internal challenges as a team.

    Trainium artwork from AWS website
    "The customer shouldn’t see or experience any of the hard work we’ve done in developing our chips; what the customer should experience is that it’s transparent and frictionless to transition to Inferentia and Trainium," says Bshara.

    The second challenge is more external; it’s the fact that science and machine learning are moving very fast. As an organization that is building hardware, our job is to predict what customers will need three, four, five years down the road because the development cycle for a chip can be two years, and then it gets deployed for three years. The lifecycle is around five years and trying to predict how the needs of scientists and the machine-learning community will evolve over that time span is difficult. Unlike CPU workloads, which aren’t evolving very quickly, machine learning workloads are, and it’s a bit of an art to keep apace. I would give ourselves a high score, not a perfect score, in being efficient in terms of execution and cost, while still being future proof. It’s the art of predicting what customers will need three years from now, while still executing on time and budget. These things only come with experience, and I’m fortunate to be part of a great team that has the experience to strike the right balance between cost, schedule, and future-proofing the product.

  5. Q. 

    At the recent re:MARS conference Rohit Prasad, Amazon senior vice president and Alexa head scientist, said the voice assistant is interacting with customers billions of times each week. Alexa is powered by EC2 Inf1 instances, which use AWS Inferentia chips. Why is it more effective for Alexa workloads to take advantage of this kind of specialized processing versus more general-purpose GPUs?


    Alexa is one of those Amazon technologies that we want to bring to as many people as possible. It’s also a great example of the Amazon flywheel; the more people use it, the more value it delivers. One of our goals is to provide this service with as low latency as possible, and at the lowest cost possible, and over time improve the machine-learning algorithms behind Alexa. When people say improving Alexa, it really means handling much more complex machine learning, much more sophisticated models while maintaining the performance, and low latency. Using Inferentia, the chip, and Inf1, the EC2 instances that actually hosts all of these chips, Alexa is able to run much more advanced machine learning algorithms at lower costs and with lower latency than a standard general-purpose chip. It's not that the general-purpose chip couldn't do the job, it's that it would do so at higher costs and higher latency. With Inferentia we deliver lower latency and support much more sophisticated algorithms. This results in customers having a better experience with Alexa, and benefitting from a smarter Alexa.

  6. Q. 

    AI has been called the new electricity. But as ML models become increasingly large and complex as you just discussed, there also are concerns that energy consumption for AI model training and inference is damaging to the environment. At the chip level, what can be done to reduce the environmental impact of ML model training and Inference?


    What we can do at the chip level, at the EC2 level, is actually work on three vectors, which we’re doing right now. The first is drive to lower power quickly by using more advanced silicon processes. Every time we build a chip in an advanced silicon process we're utilizing new semiconductor processes with smaller transistors that require less power for the same work. Because of our focus on efficient execution, we can deliver to EC2 customers a new chip based on a more modern, power-efficient silicon process every 18 months or so.

    The second vector is building more technologies, trying to accelerate in hardware and in algorithms, to get training and inference done faster. The faster we can handle training and inference, the less power is consumed. For example, one of the technologies we innovated in the last Trainium chip was something called stochastic rounding which, depending upon which measure you're looking at for some neural workloads, could accelerate neural network training by up to 30%. When you say 30% less time that translates into 30% less power.

    Another thing we're doing at the algorithmic level is offering different data types. For example, historically machine learning used a 32-bit floating point. Now we’re offering multiple versions of 16-bit and a few versions of 8-bit. When these different data types are used, they not only accelerate machine learning training, they significantly reduce the power for the same amount of workload. For example, doing matrix multiplication on a 16-bit float point is less than one-third the total power if we had done it with 32-bit floating point. The ability to add things like stochastic rounding or new data types at the algorithmic level provides a step-function improvement in power consumption for the same amount of workload.

    The third vector is credit to EC2 and the Nitro System, we’re offering more choice for customers. There are different chips optimized for different workloads, and the best way for customers to save energy is to follow the classic Amazon mantra – the everything store. We offer all different types of chips, including multiple generations of Nvidia GPUs, Intel Habana, and Trainium, and share with the customer the power profile and performance of each of the instances hosting these chips, so the customer can choose the right chip for the right workload, and optimize for the lowest possible power consumption at the lowest cost.

  7. Q. 

    I’ve focused primarily on machine learning. But let’s turn our attention to more general-purpose workloads running in the cloud, and your work on Graviton processors for Amazon EC2. 


    Yes, in a way Graviton is the opposite of our work on machine learning, in the sense that the focus is on building server processors for general-purpose workloads running in EC2. The market for general-purpose chips has been there for thirty or forty years, and the workloads themselves haven’t evolved as rapidly as machine learning, so when we started designing, the target was clear to us.

    This is an image of a Graviton silicon chip with a blue background.
    AWS is three generations into its Graviton chip journey, and Bshara says the company has plans for "many more generations" to come.

    Because this segment of the industry wasn’t moving that fast, we felt our challenge was to move the industry faster, specifically in offering step function improvement in performance, and reducing costs, and power consumption. There are many times when you build plans, especially for chips, where the original plans are rosy, but as the development progresses you have to make tradeoffs, and the actual product falls short of the original promise. With first-generation Graviton, we experienced the opposite; we were pleasantly surprised that both performance and power efficiency turned out better than our original plan. That’s very rare in our industry.

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    The same has been true with Graviton2. Because of this there has been a massive movement inside Amazon for general workloads to move to Graviton2, mainly to save on power, but also on costs. For the same workloads, Graviton2 will on average consume 60% less power than same-generation competitive offerings, and we’re passing on those cost-savings to customers. Outside Amazon, at least 48 of AWS’s top 50 customers have not just tested, but have production workloads running on Graviton2.

    In May, Graviton3 processors became available, so it’s still Day 1 as we’re only three generations into this journey. We have plans for many more generations, but it’s always very satisfying and rewarding to hear how boring it is for customers to migrate to Graviton, and to hear all the customer success stories. It is incredibly satisfying to come to work every day and hear some of the success stories from the tens of thousands of customers using Graviton.

  8. Q. 

    You have more than 100 openings on your jobs page. What kind of talent are you seeking? And what are the characteristics of employees who succeed at Annapurna Labs? 


    We are seeking individuals who like to work on cutting-edge technology, and approach challenges from a principles-first approach because most of the challenges we confront haven’t been dealt with before. While actual experience is important, we place greater value on proper thinking and a principles-first mindset, or reasoning from first principles.

    We also value individuals who enjoy working in a dynamic environment where the solution isn’t always the same hammer after the same nail. Given our principles-first approach, many of our challenges get solved at the chip level, the terminal level, and the system level, so we seek individuals who have systems understanding, and are skilled at working across disciplines. It’s difficult for an individual with a single discipline, or single domain knowledge, who isn’t willing to challenge her or himself by learning across other domains, to succeed at Annapurna. Last but not least, we look for individuals who focus on delivering, within a team environment. We recognize ideas are “cheap”, and what makes the difference is delivering on the idea all the way to production. Ideas are a commodity. Executing on those ideas is not.

  9. Q. 

    I've read that Billy and you share the belief that if you can dream it, you can do it. So what's your dream about future silicon development?


    That’s true, and it’s the main reason Billy and I wanted to join AWS, because we had a common vision that there’s so much value we can bring to customers, and AWS leadership and Amazon in general were willing to invest in that vision for the long term. We agreed to be acquired by Amazon not only because of the funding and our common long-term vision, but also because building components for our own data centers would allow us to quickly deliver customer value. We’ve been super happy with the relationship for many reasons, but primarily because of our ability to have customer impact at global scale.

    At Amazon, we operate at such a scale and with such a diversity of customers that we are capable of doing application-specific, or domain-specific acceleration. Machine learning is one example of that. What we’ve done with Aqua (advanced query accelerator) for Amazon Redshift is another example where we’ve delivered hardware-based acceleration for analytics. Our biggest challenge these days is deciding what project to prioritize. There’s no shortage of opportunities to deliver value. The only way we’re able to take this approach is because of AWS. Developing silicon requires significant investment, and the only way to gain a good return on that investment is by having a lot of volume and cost-effective development, and we’ve been able to develop a large, and successful customer base with AWS.

    I should also add that before joining Amazon we thought we really took a long-term perspective. But once you sit in Amazon meetings, you realize what long-term strategic thinking really means. I continue to learn every day about how to master that. Suffice to say, we have a product roadmap, and a technology and investment strategy that extends to 2032. As much uncertainty as there is in the future, there are a few things we’re highly convicted in, and we’re investing in them, even though they may be ten years out. I obviously can’t disclose future product plans, but we continue to dream big on behalf of our customers.

    The AWS Annapurna Labs team has more than 100 job openings for software developers, physical design engineers, design specification engineers, and many other technical roles. The team has development centers in the U.S. and Israel.

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Amazon Shipping and Delivery Support (SDS) Tech team is seeking a passionate and customer-obsessed Senior Data Scientist to join our science team. You will use scientific research and rigorous analytics to influence our program and product strategies in driver and recipient support, solve complex problems at large scale, and drive intelligence and innovation in decision making. In this role, your main focus is to perform analysis, synthesize information, identify business opportunities, provide project direction, and communicate design and technical requirements within the team and across stakeholder groups. You will assist in defining trade-offs and quantifying opportunities for a variety of projects. You will learn current processes, build metrics, educate diverse stakeholder groups, assist product and tech teams in initial solution design, and audit new process flow implementations. Key job responsibilities * Provide thought leadership and support the development of continuously-evolving business analytics and data models, own the quantitative analysis of project opportunity and ROI. * Translate difficult business problem statements into data science frameworks; build, evaluate, and optimize statistical and machine learning models to solve focused business problems. * Retrieve, analyze, and synthesize critical data into a format that is immediately useful to answering specific questions or informing operational decisions. * Collaborate with product, program, and operations teams to design experiments (A/B Test) and analyze results to support launch decisions. * Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. A day in the life If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Seattle, WA, USA
US, VA, Arlington
We are seeking a Data Scientist to join our analytics team. This person will own the design and implementation of scalable and reliable approaches to support or automate decision making throughout the business. You will do this by analyzing data with a variety of statistical techniques and then building, validating, and implementing models based your analysis. You will not be able to do this alone but by building partnerships across data, engineering, and business teams. Key job responsibilities - Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult customer or business problems and cases in which the solution approach is unclear. - Proactively seek to identify business opportunities and insights and provide solutions to automate and optimize key internal and external products based on a broad and deep knowledge of Amazon data, industry best-practices, and work done by other teams. - Dive deep into the data and other models across the business to identify defects or inefficiencies which materially impact the customer or business, but can be mitigated through corrective actions for the AB Ops use case - Acquire this data by accessing data sources and building the necessary SQL/ETL queries or scripts. - Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. - Build models and automated tools using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. - Validate these models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. - Implement these models in a manner which complies with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. - Enable product engineering teams to consume your models through services which can directly power customer-facing experiences. - Inspect the key business metrics/KPIs (even if you did not create them) when your analytics work points to potential gaps or opportunities; providing clear, compelling analyses by leveraging your knowledge across the AWS suite of products to support the broader business. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Seattle, WA, USA
GB, London
Re-imagining the realms of what’s possible in advertising. Amazon is re-imagining advertising. Amazon Ads operates at the intersection of eCommerce and advertising and offering a rich array of advertising solutions and audience insights so businesses and brands can create relevant campaigns that produce measurable results. At Amazon Ads, you can build models that impact millions every day. And we’re passionate about solving real-world problems while using cutting-edge machine learning and artificial intelligence to do this. For example, our applied science teams leverage a variety of advanced machine learning and cloud computing techniques to power Amazon's advertising offerings. This includes building algorithms and cloud services using clustering, deep neural networks, and other ML approaches to make ads more relevant while respecting privacy. They develop machine learning models to predict ad outcomes and select the optimal ad for each shopper, context, and advertiser objective, leveraging techniques like multi-task learning, bandit/reinforcement learning, counterfactual estimation, and low-latency extreme ML. The teams also utilize Spark, EMR, and Elasticsearch to extract insights from big data and deliver recommendations to advertisers at scale, continuously improving through offline analysis and impact evaluation. Additionally, they apply generative AI models for dynamic creative optimization and video experimentation and automation. Underpinning these efforts are unique technical challenges, such as operating at unprecedented scale (hundreds of thousands of requests per second with 40ms latency) while respecting privacy and customer trust guarantees, and solving a wide variety of complex computational advertising problems related to traffic quality, viewability, brand safety, and more. Help us take innovation in advertising to the next level. Our teams are based in our fast-growing tech hubs in London and Edinburgh. Learn more about Amazon Ads, employee stories and available opportunities here: Key job responsibilities * Design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both analysis and business judgment. * Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex Amazon production systems. * Promote the culture of experimentation and applied science at Amazon. * Demonstrate ability to meet deadlines while managing multiple projects. * Excel communication and presentation skills working with multiple peer groups and different levels of management * Influence and continuously improve a sustainable team culture that exemplifies Amazon’s leadership principles We are open to hiring candidates to work out of one of the following locations: Edinburgh, MLN, GBR | London, GBR
US, WA, Seattle
Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as an Applied Scientist. At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products. As an Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. The position is based in Seattle but will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions. Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Please visit for more information Key job responsibilities - Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the international seller services domain. - Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. - Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. - Continuously explore and evaluate state-of-the-art NLP techniques and methodologies to improve the accuracy and efficiency of language-related systems. - Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
GB, London
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? We are looking for a Senior Data Scientist who will be responsible to develop cutting-edge scientific solutions to optimize our Pan-European fulfillment strategy, to maximize our Customer Experience and minimize our cost and carbon footprint. You will partner with the worldwide scientific community to help design the optimal fulfillment strategy for Amazon. You will also collaborate with technical teams to develop optimization tools for network flow planning and execution systems. Finally, you will also work with business and operational stakeholders to influence their strategy and gather inputs to solve problems. To be successful in the role, you will need deep analytical skills and a strong scientific background. The role also requires excellent communication skills, and an ability to influence across business functions at different levels. You will work in a fast-paced environment that requires you to be detail-oriented and comfortable in working with technical, business and technical teams. Key job responsibilities - Design and develop mathematical models to optimize inventory placement and product flows. - Design and develop statistical and optimization models for planning Supply Chain under uncertainty. - Manage several, high impact projects simultaneously. - Consult and collaborate with business and technical stakeholders across multiple teams to define new opportunities to optimize our Supply Chain. - Communicate data-driven insights and recommendations to diverse senior stakeholders through technical and/or business papers. We are open to hiring candidates to work out of one of the following locations: London, GBR
US, VA, Arlington
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. A key focus of this role is GenAI model customization using techniques such as fine-tuning and continued pre-training to help customers build differentiating solutions with their unique data. Key job responsibilities As a Data Scientist, you will: Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Sales, Marketing and Global Services (SMGS) 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 the 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. The Professional Services team is part of Global Services. About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why 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 | Denver, CO, USA | Herndon, VA, USA | New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA | Washington Dc, DC, USA