Anton van den Hengel is seen smiling into the camera, with some office buildings in the background
Anton van den Hengel

Anton van den Hengel’s journey from intellectual property law to computer vision pioneer

Amazon’s director of applied science in Adelaide, Australia, believes the economic value of computer vision has “gone through the roof".

Anton van den Hengel, an international pioneer in computer vision and its many applications, departed the University of Adelaide in South Australia to join Amazon as director of applied science in April 2020. He is creating a new, world-class machine-learning hub in Adelaide and supporting Amazon’s business through the development and application of state-of-the-art computer vision and scalable machine learning.

Related content
Senior principal scientist Aleix M. Martinez on why computer vision research has only begun to scratch the surface.

In 2018, van den Hengel was the founding director of the Australian Institute for Machine Learning (AIML), Australia’s first institute dedicated to machine learning research. When he left to join Amazon, AIML was 140 people strong and near the top of the institutional world rankings in terms of computer vision research. He remains the part-time director of AIML’s new Centre for Augmented Reasoning, whose mission is to build core Artificial Intelligence (AI) capability in Australia.

Van den Hengel has authored more than 300 research papers, commercialized eight patents, and been chief investigator on research projects funded by many Fortune 500 companies.

But it could all have been so different. The young van den Hengel first got into computer science simply to support his efforts to become an intellectual property lawyer. In fact, he completed his law degree.

Amazon in Australia
Research teams in Adelaide are developing state-of-the-art, large-scale machine learning methods and applications involving terabytes of data. They work on applying ML, and particularly computer vision, to a wide spectrum of areas.

“I’d bought the suit, tie, and bright white shirt and was all ready to start my first day as an entry level lawyer,” he recalls. “Then, instead, I turned around and went straight back into the University of Adelaide. I spent the next couple of decades there.”

What followed was a master’s, then PhD in computer science and, ultimately, building up the University of Adelaide’s forerunner to AIML, the Australian Centre for Visual Technologies.

The chance to have an impact

What turned van den Hengel around was the chance to study computer vision.

“I saw the opportunity to engage with something that I realized was going to have incredible impact,” he says. Computer vision and its applications are everywhere today, but in the early 1990s, things were very different. “It's hard to believe now but at the time there were maybe 1000 people in the world working on computer vision, at a time when there weren't any digital cameras,” he reminisces. “Most papers in CV were at least half about how people had taken the images.”

[In the early 90s] there were maybe 1000 people in the world working on computer vision, at a time when there weren't any digital cameras. Most papers in CV were at least half about how people had taken the images.
Anton van den Hengel

Van den Hengel understood that humans are primarily visual animals and he clearly saw the inevitability of computers using vision to sense, and ultimately interact with, the world. “But back then, having a computer that could actually either measure or impact upon the real world was virtually unbelievable,” he says.

Since then, he says, computer vision has transformed from a heavily mathematical field with 300 people at every conference who all knew each other, to conferences of many thousands of people and auditoria full of companies trying to attract staff and sell things.

“The economic value of computer vision has gone through the roof,” he says.

Computer vision is a fundamental technology, van den Hengel says, because it relates the real world to symbols. “Humans reason about things in terms of symbols, so ‘cat’, ‘sky’, ‘car’, ‘road’, and ‘fish’ are all symbols, right? Computer vision takes visual signals from the real world and relates those signals to symbols,” he says.

That's been the critical missing piece of the puzzle. For decades it was predicted that by the year 2000 we would have robots doing the housework and many other ‘magical’ things, but we came up short because there's an infinite variation of things out there in the real world and it's much harder to get a computer to reason about our physical environment than anybody imagined.”

Looking for answers

This missing piece is tackled by a subfield of computer vision known as visual question answering (VQA). The idea is to enable computers not only to understand the content of an image (or video/livestream) in a more semantic, human-like way, but also to answer questions posed in natural language about that image. For example, “Where was this photo taken?”, “Does it look like the person on the picnic blanket is expecting someone?”, “What’s the color of the dog nearest the stop sign?”.

Van den Hengel is the world’s most-cited researcher in VQA by an enormous margin, with close to 22,000 citations.

Fireside chat: Anton van den Hengel and Simon Lucey

“I got into it very early because I saw it as a threshold change in the way that artificial intelligence works,” van den Hengel says. “What's interesting about VQA is that you ask the question at run-time and need the answer immediately, so it needs to be very flexible, unlike current machine learning applications, which are often fixed, single-purpose solutions to specific problems.”

In other words, it needs to be closer to true artificial intelligence – often referred to as artificial general intelligence.

In that vein, imagine a robot that could follow natural-language instructions, based on a greater understanding of what it sees around itself. It’s a sci-fi dream, but for how much longer?

In 2018, using a vision-and-language process similar to VQA, Van den Hengel and a team of colleagues from across Australia developed a simulator that uses imagery taken from the inside of real buildings to teach virtual agents to successfully navigate using visually grounded instructions, such as: “Head upstairs and walk past the piano through an archway directly in front. Turn right when the hallway ends at pictures and table. Wait by the moose antlers hanging on the wall.” It is only a matter of time before we can talk to our self-driving cars in a similar manner when necessary, says van den Hengel.

The power of neural networks

Rapid developments in machine learning are behind the recent supercharging of computer vision research.

“In the last 10 years of computer vision, we have essentially trained deep-learning neural networks to replace all of these lovely computer-vision algorithms that we'd previously come up with for solving a whole bunch of problems,” he says. “In fact, neural networks are so much better at it, they went from being just an interesting solution to a puzzle to being a practical solution to some of the core challenges we face.”

While at the University of Adelaide, van den Hengel has applied advances in ML and computer vision to make the world better in a variety of ways. These include working with Adelaide-based medical technology company LBT Innovations in creating an automated pathology machine called APAS (Automated Plate Assessment System) Independence, which can screen and interpret high volumes of pathology plates.

“There's a shortage of trained pathologists, partly because it's not a lot of fun sitting all day doing chemistry and looking at samples. APAS does the drudge work of the visual inspection process,” he says. The device was FDA approved in 2019.

Beyond computer vision, van den Hengel is currently the chief investigator for the Australian National Health and Medical Research Council’s Centre of Research Excellence in Healthy Housing, which is using ML to help deliver better outcomes within the Australian housing system, not only in terms of housing, but also in terms of health.

“People who are homeless suffer diseases and injuries, which put them into hospital, and homelessness can see people spiral into a set of difficult conditions that are very expensive for society to address,” he says. “It's actually cheaper to house somebody than to fix the impact of homelessness. So where can we intervene in the housing process in a way that benefits everybody and also saves money?”

Not all of van den Hengel’s work is quite so serious, however.

The paper I'm most happy about but that gets the least recognition is one that tells you how to build real Lego models of objects in images,” he says. “It’s got brilliant maths in it; some of my favorite maths. And it incorporates gravity, structural considerations and, you know, fantastic maths.” And did he mention the maths?

Van den Hengel has even used ML to design an IPA beer.

“Collecting the data was a real trauma: we had to drink, and rate, a lot of beer,” he laments. He named the resulting ale The Rodney, in homage to the Australian AI researcher and roboticist Rodney Brooks, whose work resulted in the Roomba vacuum cleaner.

Joining Amazon

Always an advocate for Australia on the world stage, van den Hengel was keen to play a leading role in Amazon’s research push into the country. “It was a fantastic opportunity to start a new group in Australia for a company like Amazon.”

Typically, when academics transition to Amazon, they talk about the increase in pace from academia to industry. Van den Hengel bucks that trend.

“I was running a group with 140 people, trying to make enough money to pay them, keep the doors open, deliver on projects for tens of millions of dollars, doing PR, you name it,” he says. “Here, I've got about 25 world-class people with PhDs who work for me and 12 interns.”

Van den Hengel noted that Amazon is a results-focused environment. “At Amazon you are expected to deliver, but you do it with an engineering team and support systems all geared towards delivering customer benefit.”

So what is van den Hengel delivering on? A current project is applying visual inspection methods to help to make sure that Amazon customers get the best fresh produce possible.

I think the whole retail field is moving towards a better understanding of the nature of objects in the world and how humans relate to those objects, or products. And that's something that computer vision is particularly well-placed to deliver.
Anton van den Hengel

“Visual inspection is a magnificent challenge and a core problem in computer vision,” he says,” and addressing it means we can make sure that when a customer receives a delivery of, say, tomatoes, they are as perfect as can be.”

Another key project involves using computer vision and ML to understand in a deeper way the hundreds of millions of items in the ever-changing Amazon catalogue. The catalogue has a trove of information, both in the word-based product descriptions and the images supplied by sellers.

“Making the most of the information contained in these two sources of information – which is essentially what humans do – is an interesting challenge, because it relies on the relationships between visual signals and symbols,” he explains, adding that cracking this challenge will help customers who are using Amazon search find the product that best matches their need “even if they're not entirely sure how best to specify it themselves.”

Despite the considerable demands of managing a growing team, van den Hengel is determined to remain hands-on with his own research. “Amazon's an innovative company, and really, truly innovating in a way that's going to provide something of value to customers that nobody else can means that you need managers who deeply understand where the technology can go,” he says.

So where is the technology going?

“I think the whole retail field is moving towards a better understanding of the nature of objects in the world and how humans relate to those objects, or products,” he says. “And that's something that computer vision is particularly well-placed to deliver.”

Browse through the open science positions in Amazon's Australia offices.

Research areas

Related content

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 4:14 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. 4:14 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, CA, Palo Alto
We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You’ll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! We are open to hiring candidates to work out of one of the following locations: Palo Alto, CA, 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. Salary: $163,238 - $178,400/year. 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
CN, 11, Beijing
Amazon Search JP builds features powering product search on the Amazon JP shopping site and expands the innovations to world wide. As an Applied Scientist on this growing team, you will take on a key role in improving the NLP and ranking capabilities of the Amazon product search service. Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in. We do so by developing NLP components that cover a wide range of languages and systems. As an Applied Scientist for Search JP, you will design, implement and deliver search features on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation in NLP and IR to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times. Key job responsibilities - Designing and implementing new features and machine learned models, including the application of state-of-art deep learning to solve search matching, ranking and Search suggestion problems. - Analyzing data and metrics relevant to the search experiences. - Working with teams worldwide on global projects. Your benefits include: - Working on a high-impact, high-visibility product, with your work improving the experience of millions of customers - The opportunity to use (and innovate) state-of-the-art ML methods to solve real-world problems with tangible customer impact - Being part of a growing team where you can influence the team's mission, direction, and how we achieve our goals We are open to hiring candidates to work out of one of the following locations: Beijing, 11, CHN | Shanghai, 31, CHN
DE, BE, Berlin
The Artificial General Intelligence (AGI) team is looking for a Senior Applied Scientist with background in Large Language Model, Natural Language Process and Machine/Deep learning. You will be work with a team of applied and research scientists to bring all existing Alexa features and beyond to LLM empowered Alexa. You will interact in a cross-functional capacity with science, product and engineering leaders. Key job responsibilities • Conducting research leading to improved Alexa AI systems • Communicating effectively with leadership team as well as with colleagues from science, engineering and business backgrounds. • Providing research directions and mentoring junior researchers. We are open to hiring candidates to work out of one of the following locations: Berlin, BE, DEU
US, MA, North Reading
Working at Amazon Robotics Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of doers that work passionately to apply cutting-edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Position Overview The Amazon Robotics (AR) Software Research and Science team builds and runs simulation experiments and delivers analyses that are central to understanding the performance of the entire AR system. This includes operational and software scaling characteristics, bottlenecks, and robustness to “chaos monkey” stresses -- we inform critical engineering and business decisions about Amazon’s approach to robotic fulfillment. We are seeking an enthusiastic Data Scientist to design and implement state-of-the-art solutions for never-before-solved problems. The DS will collaborate closely with other research and robotics experts to design and run experiments, research new algorithms, and find new ways to improve Amazon Robotics analytics to optimize the Customer experience. They will partner with technology and product leaders to solve business problems using scientific approaches. They will build new tools and invent business insights that surprise and delight our customers. They will work to quantify system performance at scale, and to expand the breadth and depth of our analysis to increase the ability of software components and warehouse processes. They will work to evolve our library of key performance indicators and construct experiments that efficiently root cause emergent behaviors. They will engage with software development teams and warehouse design engineers to drive the evolution of the AR system, as well as the simulation engine that supports our work. Inclusive Team Culture Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have 12 affinity groups (employee resource groups) with more than 87,000 employees across hundreds of chapters around the world. We have innovative benefit offerings and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which reminds team members to seek diverse perspectives, learn and be curious, and earn trust. Flexibility It isn’t about which hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We offer flexibility and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth We care about your career growth too. Whether your goals are to explore new technologies, take on bigger opportunities, or get to the next level, we'll help you get there. Our business is growing fast and our people will grow with it. A day in the life 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 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! We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA
US, MA, Boston
The Artificial General Intelligence (AGI) - Automations team is developing AI technologies to automate workflows, processes for browser automation, developers and ops teams. As part of this, we are developing services and inference engine for these automation agents, and techniques for reasoning, planning, and modeling workflows. If you are interested in a startup mode team in Amazon to build the next level of agents then come join us. Scientists in AGI - Automations will develop cutting edge multimodal LLMs to observe, model and derive insights from manual workflows to automate them. You will get to work in a joint scrum with engineers for rapid invention, develop cutting edge automation agent systems, and take them to launch for millions of customers. Key job responsibilities - Build automation agents by developing novel multimodal LLMs. A day in the life An Applied Scientist with the AGI team will support the science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience.; while setting examples for the team on good science practice and standards. Besides theoretical analysis and innovation, an Applied Scientist will also work closely with talented engineers and scientists to put algorithms and models into practice. We are open to hiring candidates to work out of one of the following locations: Boston, MA, USA
US, WA, Seattle
Are you motivated to explore research in ambiguous spaces? Are you interested in conducting research that will improve the employee and manager experience at Amazon? Do you want to work on an interdisciplinary team of scientists that collaborate rather than compete? Join us at PXT Central Science! The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are seeking a senior Applied Scientist with expertise in more than one or more of the following areas: machine learning, natural language processing, computational linguistics, algorithmic fairness, statistical inference, causal modeling, reinforcement learning, Bayesian methods, predictive analytics, decision theory, recommender systems, deep learning, time series modeling. In this role, you will lead and support research efforts within all aspects of the employee lifecycle: from candidate identification to recruiting, to onboarding and talent management, to leadership and development, to finally retention and brand advocacy upon exit. The ideal candidate should have strong problem-solving skills, excellent business acumen, the ability to work independently and collaboratively, and have an expertise in both science and engineering. The ideal candidate is not methods-driven, but driven by the research question at hand; in other words, they will select the appropriate method for the problem, rather than searching for questions to answer with a preferred method. The candidate will need to navigate complex and ambiguous business challenges by asking the right questions, understanding what methodologies to employ, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders). About the team We are a collegial and multidisciplinary team of researchers in People eXperience and Technology (PXT) that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We leverage data and rigorous analysis to help Amazon attract, retain, and develop one of the world’s largest and most talented workforces. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Austin, TX, USA | Chicago, IL, USA | New York, NY, USA | Seattle, WA, USA | Sunnyvale, CA, USA