Eugene Yan
Eugene Yan is an applied scientist at Amazon, but he’s also known for his personal site where he covers topics like machine learning systems, data science methodology, dealing with imposter syndrome, and building data science teams.
Courtesy of Eugene Yan

Eugene Yan and the art of writing about science

Why the Amazon applied scientist takes the time to break down his work for readers.  

Eugene Yan’s career path has taken some unusual turns, but his motivation has always been the same: understanding people so he can help them. A policy analyst turned data scientist, Yan is now an applied scientist at Amazon using customer-behavior data to help recommend the best products. In the world of machine learning, however, he’s best known for the way he writes it all down. On his personal site, Yan covers a range of professional and technical topics like machine learning systems, data science methodology, dealing with imposter syndrome, and building data science teams.

Eugene Yan started eugeneyan.com in 2020, focusing on general machine learning and career content. Initially it was for personal development, but then people started reading, and now writing posts takes up the majority of his leisure time.

He started the site for personal development, but then people started reading, his network started expanding, and now writing posts takes up the majority of his leisure time. “It snowballed,” he said. “Writing helps me learn better. And when I share it online, it attracts like-minded readers and helps me make new friends. ”

Born in Singapore, Yan studied psychology at Singapore Management University. “I was curious about people, how they perceive and how they behave,” he said. His college research focused on how competition affects people differently — motivating some and intimidating others. After college, he joined the Singapore government as a policy analyst sifting through legal cases and trade agreements. But it wasn’t long before he began to miss crunching numbers and following the data on human behavior. “I began to envy my colleagues in commodities who relied on numbers and worked with spreadsheets,” Yan said.

He decided to try and make the switch to a data science position based on some familiarity with the subject from his undergraduate research days. He landed a position at IBM in 2013, and from there he moved to data science roles at Lazada, a Southeast Asian e-commerce site, and then UCARE.AI, a healthcare startup.

A desire to help

“In every change in my career, what drives me is helping people,” Yan noted.

At IBM, it was helping people find new roles. At Lazada, it involved helping people find products they need. At UCARE.AI, it entailed predicting chronic diseases and preventing high insurance payouts. “This brings me way more satisfaction than dollars and cents,” he explained.

While at Lazada, Yan decided he needed more training in the fundamentals and pursued a master’s in computer science from the Georgia Institute of Technology. He graduated in 2019, and then he and his wife began considering a move overseas. He applied for a position at Amazon, drawn to the company’s leadership principles and the ability to help customers read more. He relocated to Seattle to join Amazon in 2020.

While Amazon has several ways to help readers find books, from Amazon Book Review to Amazon Charts, Yan is part of a team developing the recommendation systems that power the widgets behind the Amazon Store’s personalized book suggestions. “Customers tell us what they like based on what they do,” he explained. “They browse for a specific book, a genre or a topic.” His team uses those signals to help surface additional books a reader might like. Ultimately, Yan and his team want to make reading easier.

Writing it all down

Early in his transition to data science, Yan started interviewing mentors for advice, some of whom were “rock star data scientists.” He asked what skills he should cultivate to be successful. The one skill a majority of mentors suggested was communication. The people he spoke with emphasized how communication becomes more and more important as you rise in the ranks. “I was like, ‘Are you kidding me?’ But more and more mentors said the same thing,” he recalls. “I thought, ‘This can’t be right, but I'm just going to try it.’”

Yan started practicing his writing, first publishing to a WordPress site. He wrote dozens of posts unnoticed, but then in 2020 created eugeneyan.com and started writing more general machine learning and career content. His writing began to gain an audience. Posts like “Unpopular opinion — Data scientists should be more end-to-end” received more than 500 likes on Twitter. One post on note-taking received 35,000 unique views in a single day. Feedback and praise began to pour in, and his “practice” website swelled into something much bigger.

For a brief period, Yan tried to sustain this level of social engagement. He wrote to please a mass audience and get clicks. “That quickly became unfulfilling,” he said. Now he focuses his writing on topics he wants to learn and aims for an audience of people he’d hope to be friends or colleagues with. “I might have fewer readers now since I’m choosing more technical topics, but these readers comment, disagree, and email me. Each comment and real relationship are worth more than 10,000 likes,” he said.

The many benefits of good writing

Yan's decision to become a better communicator and writer is especially valuable at Amazon. “The writing culture is rigorous at Amazon,” he said.

Amazon’s working backward method starts with an individual or team imagining the product or service is ready to launch. The individual or team’s first step is to draft a press release announcing the product’s availability, and explaining its significance. Moreover, meetings often start with participants reading a six-page document about the meeting’s topic before discussion begins.

Finding your voice and niche doesn’t happen overnight — you have to write and share your work. So just start somewhere, anywhere, and keep writing.
Eugene Yan

“I write as many documents as I code,” Yan said. Recently he received feedback that one of his design documents was easy to understand and clearly laid out everything the reader needed to know. In this way, his writing skills complement his design and machine learning skills. He also started a new site, Applying ML, which includes interviews with machine learning practitioners.

Yan is often asked by aspiring writers for advice on how they can improve their skills. The number one piece of direction he offers is to write for yourself — what do you want to learn and clarify your thinking on? — rather than social engagement. The second piece of advice: “just write.” The best way to figure out your niche and your audience is to simply put fingers to keyboard and start practicing, he said. Maybe after a dozen — or a few dozen — pieces you find your voice, what you want to write about, or what resonates best with the people reading along.

“If you never start writing, how will you know? Just like Blue Origin’s motto ‘Gradatim Ferociter’, which means ‘Step by step, ferociously’. Finding your voice and niche doesn’t happen overnight — you have to write and share your work,” he said. “So just start somewhere, anywhere, and keep writing.”

Research areas

Related content

US, CA, San Francisco
The Amazon General Intelligence “AGI” organization is looking for an Executive Assistant to support leaders of our Autonomy Team in our growing AI Lab space located in San Francisco. This role is ideal for exceptionally talented, dependable, customer-obsessed, and self-motivated individuals eager to work in a fast paced, exciting and growing team. This role serves as a strategic business partner, managing complex executive operations across the AGI organization. The position requires superior attention to detail, ability to meet tight deadlines, excellent organizational skills, and juggling multiple critical requests while proactively anticipating needs and driving improvements. High integrity, discretion with confidential information, and professionalism are essential. The successful candidate will complete complex tasks and projects quickly with minimal guidance, react with appropriate urgency, and take effective action while navigating ambiguity. Flexibility to change direction at a moment's notice is critical for success in this role. Key job responsibilities Key job responsibilities Serve as strategic partner to senior leadership, identifying opportunities to improve organizational effectiveness and drive operational excellence Manage complex calendars and scheduling for multiple executives Drive continuous improvement through process optimization and new mechanisms Coordinate team activities including staff meetings, offsites, and events Schedule and manage cost-effective travel Attend key meetings, track deliverables, and ensure timely follow-up Create expense reports and manage budget tracking Serve as liaison between executives and internal/external stakeholders Build collaborative relationships with Executive Assistants across the company and with critical external partners Help us build a great team culture in the Lab!
IN, KA, Bengaluru
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As a Data Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Data Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.
US, CA, Sunnyvale
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Science Manager to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will lead a strong science team and work closely with other science and engineering leaders, product and business partners together to build the best personalized customer experience for Prime Video. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Lead to develop AI solutions for various Prime Video recommendation and personalization systems using Deep learning, GenAI, Reinforcement Learning, recommendation system and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Hire and grow a science team working in this exciting video personalization domain. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various devices. We work closely with the engineering teams to launch our solutions in production.
US, CA, San Francisco
We are seeking a Member of Technical Staff Simulation Engineer to join our AI robotics research team developing foundation models for robotics. You will rapidly develop 3D physics-based and photorealistic simulations alongside scientists to enable training large-scale machine learning models. Key job responsibilities - Develop simulations for reinforcement learning, closed-loop simulations and synthetic data generation - Implement essential robotics features, including accurate modeling of sensors, actuators, and controllers - Build real-to-sim workflows for dynamic environments and robotics tasks - Implement simulation features to minimize sim-to-real gaps through domain randomization and system identification - Create asset toolchains supporting industry-standard formats (URDF, MJCF, USD) - Collaborate closely with a team of ML researchers to enable large-scale robotics training pipelines About the team At Frontier AI & Robotics (FAR), we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
IN, KA, Bengaluru
RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will independently file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.
US, MA, N.reading
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. We are seeking a talented Applied Scientist to join our advanced robotics team, focusing on developing and applying cutting-edge simulation methodologies for advanced robotics systems. This role centers on research and development of physics-based simulation techniques, sim-to-real transfer methods, and machine learning approaches that enable rapid development, testing, and validation of robotic systems operating in complex, real-world environments. Key job responsibilities - Advance physics-based simulation fidelity for contact-rich manipulation and locomotion - Design and build high-performance simulation tools integrated into a robotics design stack - Translate research ideas into robust, verifiable data - Develop methods to quantify and reduce simulation-to-reality gaps across design, safety, and control - Architect scalable simulation solutions for rigid and deformable body dynamics - Build simulation pipelines optimized for a digital twin level of fidelity - Establish frameworks for continuous simulation improvement using real-world hardware - Collaborate with engineering, science, and safety teams on simulation requirements and validation About the team Our team is building a comprehensive robot simulation and modeling platform for advanced robotics development, combining locomotion and manipulation capabilities. We operate at the cutting edge of physics simulation, reinforcement learning, hardware-in-the-loop (HIL), and sim-to-real transfer, collaborating with world-class robotics engineers, scientists, and mechanical designers in a fast-paced, innovation-driven environment. This role uniquely combines fundamental research with real-world development. You will pursue core research questions in physics-based simulation while seeing your work translated into real robots, validated on real hardware. Working alongside Robot scientist and designers, you will help transform research ideas into scalable, quantifiable simulation capabilities that directly impact how robots are designed and built.
US, CA, East Palo Alto
As part of the AWS Solutions organization, we have a vision to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon’s real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. Key job responsibilities Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company. We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems. As a Sr. Applied Scientist, you will help solve a variety of technical challenges and mentor other scientists. You will be the thought leader of the team. You will tackle challenging, novel situations every day and given the size of this initiative, you’ll have the opportunity to work with multiple technical teams at Amazon in different locations. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems that, frankly, haven’t been solved at scale before - anywhere. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people. A key focus of this role will be developing and implementing advanced visual reasoning systems that can understand complex spatial relationships and object interactions in real-time. You'll work on designing autonomous AI agents that can make intelligent decisions based on visual inputs, understand customer behavior patterns, and adapt to dynamic retail environments. This includes developing systems that can perform complex scene understanding, reason about object permanence, and predict customer intentions through visual cues. About the team Just Walk Out (JWO) is a new kind of store with no lines and no checkout—you just grab and go! Customers simply use the Amazon Go app to enter the store, take what they want from our selection of fresh, delicious meals and grocery essentials, and go! Our checkout-free shopping experience is made possible by our Just Walk Out Technology, which automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your account and send you a receipt. Check it out at amazon.com/go. Designed and custom-built by Amazonians, our Just Walk Out Technology uses a variety of technologies including computer vision, sensor fusion, and advanced machine learning. Innovation is part of our DNA! Our goal is to be Earths’ most customer centric company and we are just getting started. We need people who want to join an ambitious program that continues to push the state of the art in computer vision, machine learning, distributed systems and hardware design.
US, CA, Palo Alto
We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. Key job responsibilities - Design and build agents for our autonomous campaigns experience. - Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). - Curate datasets and tools for MCP. - Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Prototype and iterate on multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Autonomous Campaigns team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware campaign creation and management system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company.
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company.