AfroTech logo and headshots of  Dela Agbemabiese, Justin Barry, Nashlie Sephus and Colby Wise.
With AfroTech World occurring this week, we asked some of the company's Black scientists what they consider some of the systemic issues limiting underrepresented minorities from being more involved in the technology industry. We heard from Dela Agbemabiese (lower left), a data scientist, Justin Barry (upper left), applied scientist, Nashlie Sephus (upper right), applied science manager, and Colby Wise (lower right), senior deep learning scientist.
Credit: Glynis Condon

Issues of racial, ethnic and gender diversity are on the agenda at AfroTech World

Amazon scientists provide insights on issues related to lack of involvement of underrepresented minorities in the technology industry.

As CNBC reported earlier this year, six years after initially disclosing diversity reports, major technology companies have made little progress in hiring more minorities, especially Black employees with science and technology skills.

This presents a series of ongoing challenges. According the US Bureau of Labor Statistics (BLS), nearly one-quarter of the country’s total economic output is produced by high-tech industries, and in 2017 BLS projected there would be more than 1 million job openings in computer and information technology over the next 10 years. Moreover, computing occupation salaries are more than twice the median wage for all other occupations, according to BLS.

“When we look at tech and its impact on our economy, and the simultaneous underrepresentation of the Black community, it is a critically important racial and economic justice issue," says Allison Scott, CEO of the Kapor Center. “When the tech workforce and leadership reflects the diverse experiences and backgrounds of our nation, I believe tech can begin to play an integral role in addressing long-standing disparities that exist in this country.”

As of December 31, 2019, Amazon reported that 26.5% of its global workforce identifies as Black/African American, 26.5% Asian, 18.5% Hispanic/Latinx, 1.3% as Native American, and 3.6% as two or more races.  The 26.5% of employees who identify as Black/African American work in both non-technical and technical roles.

This week at AfroTech World, issues related to the lack of adequate racial, ethnic, and gender diversity within the technology industry are on the agenda as leaders in technology and business come together to exchange ideas for creating greater opportunity for Blacks in technology.  Amazon is a Diamond Sponsor of this year’s event, and has a virtual recruiting booth.  

On Nov. 13, the company is hosting a virtual event, “Our Voices, Our Power”, presented by Amazon’s Black Employee Network (BEN) affinity group. Attendees will hear employees share their Amazon journey stories, learn about career opportunities, and enjoy entertainment.

In advance of AfroTech, Amazon Science asked some of the company’s Black scientists what they consider some of the systemic issues limiting underrepresented minorities’ involvement in the technology industry, about some of the issues they have had to overcome in pursuing their science careers, who or what inspired them to pursue their science careers, and what lessons we might take from their individual experiences. 

Dela Agbemabiese is a data scientist within Amazon’s advertising organization. He earned his master’s degree in business administration from Drexel University.

Dela Agbemabiese
Dela Agbemabiese

What do you consider some of the systemic issues limiting underrepresented minorities from greater employment opportunities in the technology industry?

Lack of financial resources to stimulate curiosity in tech, lack of mentors or heroes to look up to due to low representation, and societal prejudice hindering opportunities.

Lack of financial resources to stimulate curiosity in tech. I have been fortunate and blessed my entire life.  All gratitude goes to my parents. I was born in Ghana, West Africa. My mom was a nurse, and my dad an economist. Due to the nature of my dad’s work, I got the opportunity to travel a lot as a kid, got enrolled into a course at eight years old to get a Linux command line certificate, and always had access to tech resources. My parents sacrificed to ensure I attended the best schools, and there is not a single thing I ever asked for that I did not get. This may not be the case for all children, whose parents are possibly working hard doing multiple jobs, and in some cases are single parents. If the financial resources I had were similar to that of many minority children, it would be unlikely for me to be where I am today.

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Lack of mentors or heroes to look up to due to low representation. While my dad was heavy on econometrics and I learned a thing or two from him, it was my cousin Martey to whom I looked up. He was brilliant academically, and I always wanted to be like him. He tutored me in math and physics, thus giving me an edge over my classmates. Martey was not my only mentor, in fact, I had many, including Yao Obeng, who helped me nurture my creativity and problem-solving skills. Many minority children may not have mentors or heroes within tech to encourage and inspire interest in tech-related careers. If I did not have these mentors to motivate me, it would be unlikely for me to be where I am today.

Societal prejudice hindering opportunities. Growing up in Ghana, prejudice did not exist from a racial standpoint. Once I moved to the United States for my undergraduate degree, this became a reality. My minority friends and I have had to work twice as hard as our peers to prove we are as good as our credentials. We strived to invalidate stereotypes about minorities through the quality of our work and our work ethic. With everything I do, in the back of my mind I am thinking about how my actions or inactions affect the perception towards minorities: am I enabling some of these unfounded prejudices? Or am I, through my work, educating my peers and superiors? For me, this societal prejudice only began when I came to the United States for my undergraduate degree, but imagine the minority children out there who have had to live with this their entire lives. It sure can get demoralizing.

What are some of the obstacles you had to overcome in pursuing your science career?

Societal prejudice hindering opportunities. I have been lucky to have managers and peers that are inclusive and open-minded, that judge me based on the quality of my work. Rachel McKitrick was my first manager in Amazon. I joined Amazon as a business analyst, despite my previous role as a senior data scientist. I just wanted to join Amazon! Rachel knew my business analyst role was not ideal, and gave me projects that were science oriented, which ultimately enabled my transition to scientist. My second manager, Monica Wu, always made herself available to chat and made me feel like my voice and opinion mattered. My current team managed by Dauwe Vercamer and Andrew Petschek welcomed me with open arms, gave me opportunities to shine and lead within the team. They provide direct feedback that has made me a much better scientist today.

I have had the privilege of learning from a lot of people. Societal prejudice may be harder to solve for, but I believe a good place to start will be to find means for minority youth to gain access to some of the brilliant minds within the technology industry, be it through some virtual teaching programs, or through some mentoring programs. The prejudice may exist, the financial resources may be sparse or non-existent, but with heroes and mentors to look up to, a child’s imagination can be sparked for what could be.

Who or what inspired you to pursue your science career, and what lessons can we take from your experience?

My dad due to his econometrics background, and my childhood mentors who encouraged me to put math and science ahead of basketball and soccer. Since then, I have had lots of mentors along the way, especially here at Amazon. Individuals such as Leo Razoumov, Pranjal Mallick, Amy Ruschak, John Lafayette, and Oded Netzer, who have helped shape me into a better scientist.

My advice to Black students interested in a STEM career, or other Black scientists is to find mentors, and get them involved in your work. Meet with them once a week for even 10 minutes, and let them influence your work.

Justin Barry is an applied scientist with Amazon’s Prime Video organization. He earned his master’s degree in computer science from the University of Central Florida.

Justin Barry
Justin Barry

What do you consider some of the systemic issues limiting underrepresented minorities from greater employment opportunities in the technology industry?

This is a massive topic with a myriad of associated socioeconomic issues. One issue that jumps to the forefront for me is the schools where leading companies within the tech industry recruit from. Traditionally, these companies have limited their recruitment to top universities where Blacks and other underrepresented minorities comprise a small percentage of the student population. This is beginning to change, but I believe technology companies need to more aggressively expand their recruitment efforts, especially among historically Black colleges and universities (HBCUs).

What are some of the obstacles you had to overcome in pursuing your science career?

One issue is imposter syndrome — the idea that you're not good enough and you’re only in your position because you’ve been given special treatment. Although imposter syndrome is something everyone experiences, I think it’s particularly acute for Blacks given the clear underrepresentation within the technology industry. Imposter syndrome can touch all aspects of your job if you’re unaware, or if you don’t have the tools to deal with it. Not everyone has the tools to deal with it, and I suspect not everyone has correctly identified the problem.

Who or what inspired you to pursue your science career, and what lessons can we take from your experience?

Video games sparked my interest in computer science, and more specifically artificial intelligence. My undergraduate degree is in computer science and math, and machine learning and AI provide the opportunity to apply my computer science and math skills to real-world applications.

Nashlie Sephus is an applied science manager within Amazon Web Services Ai. She earned her PhD in electrical and computer engineering from Georgia Tech.

Nashlie Sephus
Nashlie Sephus

What do you consider some of the systemic issues limiting underrepresented minorities from greater employment opportunities in the technology industry?

Imposter syndrome is one issue I find common within underrepresented minority groups. It’s a feeling of being convinced that you don’t belong in the industry, or within advanced roles in the industry, regardless of your accolades and accomplishments. It is as if they are not real or didn’t happen. This is often due to not seeing many others who look like you in similar or higher positions. ‘You can’t be what you can’t see’ is a common thought. Also, there are few mentors or support systems for these groups, and as a black/female/engineer/scientist, you sometimes feel like the minority of the minority, which further isolates you.  

What are some of the obstacles you had to overcome in pursuing your science career?

At times, I have had to fight for myself and members of my teams for equal pay and advancement in my career. I also have needed to develop mechanisms to be heard when it was difficult to convey messages to those around me. I’m usually quiet and reserved, but over the years I’ve learned how to gain respect from peers by being more outspoken even, or especially, when I disagreed. This is one reason why I appreciate Amazon’s leadership principle: Have Backbone; Disagree and Commit. 

Who or what inspired you to pursue your science career, and what lessons can we take from your experience?

I grew up in a house full of women where we often did our own chores, like fixing and repairing things around the house. I was also always going to summer math and science camps in elementary and middle school, especially a summer engineering camp for girls after my eighth grade science teacher recommended I attend. This was when I was first introduced to the various areas of engineering, and fell in love with computer science. Being able to control the hardware with software was fascinating to me. I knew then that’s what I wanted to do. This early exposure to science was key to me figuring out one of my passions, in addition to music and sports.

Colby Wise is a senior deep learning scientist and manager within the AWS Machine Learning Solutions Lab. He earned his master’s degree in computer science from the Columbia University Fu Foundation School of Engineering and Applied Sciences.

Colby Wise
Colby Wise

What do you consider some of the systemic issues limiting underrepresented minorities from greater employment opportunities in the technology industry?

Educational opportunity. Science, technology, engineering, and math (STEM) careers in the technology industry are highly competitive. Over the years, we’ve seen advanced tools and technologies like cloud technology, machine learning, and deep learning, that were once reserved only for large companies or prestigious universities being utilized by students as early as junior high school. While this has created and accelerated educational opportunities for millions of students globally, the reality is that not all have been able to benefit. In the United States, public school funding varies significantly by geography, and where you grow up is a major factor in access to educational resources. Schools with advanced STEM courses and other after-school programs are valuable inroads for STEM students to accelerate their learning opportunities and explore careers in science. What’s more, these opportunities compound positively from lower educational levels to higher educational levels. While not the only factor, these programs are important when understanding the pipeline of underrepresented minorities in highly competitive industries like technology. For example, the US Federal Reserve conducted a study highlighting how educational attainment of parents plays an important role in children’s educational pursuits. Studies like this and others indicate that lower parental educational attainment may present a unique challenge for students. One potential consequence of underrepresentation of minorities in advanced degrees is that employment opportunities often arise from one’s social network, employee referrals, for example. This can be summarized as both an employment funnel problem and a network problem. While not always the case, a more diverse workforce can build connections to underrepresented talent pools. 

Financial equality. In a study from 2020, the US Federal Reserve found large and persistent gaps in net wealth and earnings by race and ethnicity. While education is a significant factor in wage gaps, the St. Louis Federal Reserve found net wealth by race was not as positively correlated with educational attainment for minorities. Educational attainment is extremely important. Many highly technical roles require advanced degrees. Financial equality and opportunity as characterized by job salary prospects, current income and net wealth, and access to educational funding sources like loans are all potential factors impacting lower minority employment. In 2016, the Brookings Institution found the median household net wealth for Black and Hispanic families to be 1/8th  that of white households. When you consider the rising cost of college and advanced degrees, this income and net wealth gap may also play a factor in why employment among underrepresented minorities is lower in highly competitive industries like technology. Specifically, minorities whose households cannot readily pay for advanced degrees choose between the implications of high debt burdens and lower comparative earnings, and often must forsake advanced degrees to enter or stay in the workforce.

Leadership representation. Representation of minorities in leadership positions is relatively low. It is unclear how much educational opportunity and financial equality contribute to this, compared to other issues such as equitable pathways to senior leadership positions. In many companies in which I have worked, you notice a similar triangular pattern of minority leadership where representation at junior levels is more in-line with industry trends, while there is a dearth of representation as you reach more senior positions. No doubt there is work to be done to drive greater employment of underrepresented minorities at all levels. But simply increasing the representation at entry levels does not address other attrition and talent-retention hurdles. Overall, companies need to take a more systematic, data-driven approach to move the needle and find solutions to underrepresentation of minorities in the tech industry. For instance, companies should not be afraid to tackle the complex issues at multiple hierarchies, such as creating innovative solutions to drive educational opportunity while objectively measuring current pathways to employment within the tech industry. Furthermore, companies should ensure financial equality by aligning corporate incentives with fair pay distributions, minority leadership representation, and talent development and retention.  

What are some of the obstacles you had to overcome in pursuing your science career?

Educational opportunity. While everyone’s path is different, unfortunately my story is rather common given its similarity to those of many underrepresented minorities. I faced and overcame obstacles in educational and financial opportunity plus roadblocks to leadership roles. I attribute my luck mainly to the many individuals who provided a helping hand, plus a little bit of hard work sprinkled in. I grew up in a single-parent household in an impoverished, high-crime inner-city area. Despite this, my family valued education highly, and one of my parents had an advanced degree which was extremely rare for the area. Given that, I always ranked in the top 1% in my coursework while very young. That said, district educational attainment rates were low, and advanced coursework or programs for gifted students were nonexistent. However, prior to high school an unfortunate family event led to me moving from one of the poorest areas in the country to one of the best school districts nationally. After discovering how far behind I was in math and science, my family and I worked extremely hard over several years to get me back in line with my expected academic grade level. Now fast-forwarding to college: I, like many other minorities, did not have the means to pay for college, nor easy access to loans. After being selected to a number of great schools, my decision was ultimately driven by the amount of money I received in scholarships and grants. During college I followed the same recipe for success: tons of luck, humility to ask for help, and a bit of hard work to land an internship as a sophomore at a prestigious Wall Street investment bank. There I was surrounded by some of the smartest minds in STEM, with many having achieved advanced degrees from top universities around the world. The vast majority of these individuals did not look like me. Desperately wanting to be accepted and succeed among my peers in industry is what drove me to pursue a career in science, and many years later brought me to AWS.

Who or what inspired you to pursue your science career, and what lessons can we take from your experience?

Family and friends. Ultimately, doing what you love and constantly learning while being curious is the greatest inspiration one needs to pursue a career in science. As discussed above, studies have shown a correlation between parental educational attainment and children’s attainment. Thinking forward a bit, I combined my passion for what I love in science — AI/ML — with a selfish goal of wanting to be a living model for a career in science for my children. My greatest inspiration, however, is my wife. She discovered her passion for science at a very young age with plentiful opportunities to explore that passion, ultimately helping her reach the pinnacles of academia, where she received undergraduate and graduate degrees from two of the top universities in the world. Her passion for science, hard work, and humility continue to inspire me on a daily basis.

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Verify closed-loop performance in hardware-in-the-loop (HITL) tests. - Support component environmental tests, functional and performance checkout, and subsystem and satellite integration in our labs. - Analyze telemetry from the whole fleet. Build the metrics, dashboards and tools that track pointing performance and payload operations time as the constellation grows, and use them to improve the fleet. - Operate the GNC system on orbit and take part in the team's on-call rotation. Review fleet data, support maneuvers, and resolve anomalies when on-orbit behavior differs from the ground model. - Review designs and code with the team, and document your algorithms, analyses and results clearly. A day in the life This role covers a wide range of problems across the spacecraft and the network. You will derive and prototype estimation and control algorithms, and then turn them into production flight code. You will prove them in 6-DOF and Monte Carlo simulation and in hardware-in-the-loop tests in the lab. You will support satellite integration. When a satellite does not behave as the ground model predicted, you will dig into fleet telemetry, find the cause and drive the fix. You will own each problem end to end, with the focus to go deep on it. GNC is at the center of safe flight, so you will work across the whole program. The payload, power, thermal and operations teams all depend on GNC, and you will balance their needs against the pointing and momentum budgets. You will partner closely with avionics, flight software and embedded software engineers to make your algorithms run reliably on the spacecraft. You will help the mechanical teams shape the next generation of satellite hardware, from reaction wheels to star trackers. As satellites move from design through manufacturing into orbit, you will work with systems engineering and Assembly, Integration and Test. The problems are hard: tight pointing requirements, a large constellation to keep flying safely, and the chance to invent capabilities that do not exist yet. Constant work with other teams makes this a place where you keep learning and growing. About the team You will join the Satellite Attitude Determination and Control team. We own GNC for our satellites end to end: algorithm design, flight software, lab integration, and flight operations of our own satellites on orbit. Our engineers bring deep experience from many satellite programs and other flight vehicles, with real strength in both our mission and the core GNC disciplines. We design, prototype, test, iterate and learn together. Because GNC is central to safe flight, we drive the concepts of operations and many of the system-level analyses on the program. We also have a lot of freedom. We trust our engineers to choose their own tools and approaches for the problems they face, and to own the results. If you want the autonomy to solve hard problems your way, with a team of strong engineers, you will feel at home here.
US, WA, Seattle
Do you want to make AI provably correct for chip design and verification? We're hiring an Applied Scientist to combine large language models with formal methods such as Lean 4 and turn AI from a helpful assistant into a trusted engineering tool for silicon. Our team builds the formal verification methods behind custom silicon at Amazon scale. As AI agents take on more of chip design and verification, we need people who can make sure their output is checkable, sound and grounded in mathematics. You will research and build systems where LLMs work alongside interactive theorem provers, SMT solvers and model checkers. Engineers write the specifications. AI refines them against RTL, generates proof steps and closes proofs at machine speed. Every result is checked by a formal kernel, not trusted on faith. Work at the frontier of AI reasoning, theorem proving and hardware verification Ship solutions that directly affect real chips running in AWS data centers Publish and help shape how the industry formalizes AI use in hardware engineering Key job responsibilities Design and build LLM-driven systems for formal verification and design tasks, including proof generation, invariant and lemma discovery, spec-to-RTL refinement, and counterexample analysis Develop frameworks in Lean 4 (and related tools such as SMT solvers, model checkers and commercial FV engines) that formally check AI-generated artifacts, so they are correct by construction rather than just plausible Formalize hardware semantics, ISAs and microarchitectural properties in Lean 4 to create machine-checkable foundations for AI agents to reason over Track and evaluate state-of-the-art AI work (LLM agents, autoformalization, neural theorem proving, reinforcement learning from proof feedback) and turn promising research into production tools Build benchmarks and evaluation methods that measure AI soundness, proof success rate and engineering impact on real designs Partner with FV and design engineers to find the highest-leverage problems and deploy solutions into active chip programs Publish research, file patents and represent Amazon in the formal methods and AI communities Mentor engineers and scientists on applying formal AI methods
US, CA, Pasadena
We are seeking Applied Scientists to join Amazon. In this role, you will develop and deploy the science of safe autonomy on highly dynamic robots, advancing the frontier through both foundational research and realization on hardware. You will contribute to achieving safe autonomy across three key areas: control barrier function (CBF) theory for robust and performant safety on hardware; safe reinforcement learning for agile whole-body control; and layered safety filters that interface with learning, perception, and semantic reasoning systems. You will validate your work experimentally on state-of-the-art robotic platforms — removing bottlenecks to deployment and enabling robots to safely operate around humans. You will work with the inventor of CBFs, as well as top scientists and engineers at Amazon developing the next generation of safe autonomy. Key job responsibilities -Advance the science of safe autonomy from formal foundations to the integration with learning and perception to validation on hardware — with particular emphasis on methods that bridge these domains. -Test and validate hardware, with a focus on next generation robotic systems. Including locomotion, manipulation and loco-manipulation. -Leverage the deployments on hardware to validate the underlying science, identifying gaps between theory and practice that drive the next cycle of research. hardware to validate the underlying science, identifying gaps between theory and practice that drive the next cycle of research. -Publish research at top-tier robotics, control, and ML venues, and contribute to Amazon’s scientific reputation in advanced robotics. -Collaborate with SAF Lab and Amazon production teams to move research into robots deployed at Amazon scale. 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: -Medical, Dental, and Vision Coverage -Maternity and Parental Leave Options -Paid Time Off (PTO) -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! About the team The Safe Autonomy Frontiers (SAF) Lab is the first industry research lab dedicated to safe autonomy, founded by the inventor of control barrier functions. We are developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, quadrupeds, and humanoids. Here you will advance performant safety for highly dynamic robots — CBF theory integrated with perception and learning, evaluated on next-generation platforms — and your work will underpin robots operating alongside people at Amazon’s unprecedented scale.
US, MA, North Reading
Robotics Applied Scientist II Intern/Co-op (2027) Are you passionate about robotics and research? Do you want to solve real customer problems through innovative technology? Do you enjoy working on scalable research and projects in a collaborative team environment? Do you want to see your science solutions directly impact millions of customers worldwide? We’re looking for current PhD students with a passion for robotic research and applications to join us in 2027 as Robotics Applied Scientist II Interns/Co-ops to shape the future of robotics and transform automation at Amazon’s scale. By applying to this role, you will be considered for Robotics Applied Scientist II Intern/Co-op (2027) opportunities across various Robotics teams at Amazon with different robotics research focus. Internship positions are available for multiple locations, durations (3 to 6+ months), and year-round start dates (winter, spring, summer, fall). Join us at the forefront of Applied Science, where your contributions will shape the future of Robotics! Key job responsibilities As a Robotics Applied Scientist II Intern/Co-op, you will work alongside global experts to advance the state of the art in robotics and artificial intelligence, tackling innovative problems that bridge the gap between research and real-world robotics implementation to innovate and build solutions on behalf of our customers. We are particularly interested in students with specialization and expertise in one or more of the following research areas: - Robotics - Artificial Intelligence - Robotics Manipulation (e.g., robot arm, grasping, dexterous manipulation, end of arm tools/end effector) - Autonomous Mobile Robots - Mobile Manipulation - Multi-Robot Coordination - Autonomous Navigation - Locomotion - Motion/Path Planning - Controls - Perception - Sensing - Robot Learning - Machine Learning - Computer Vision - Large Language Models (LLM), Vision-Language-Action Models (VLA), Vision-Language-Models (VLM) - World Models - Human-Robot Interaction - Robotics Simulation - Optimization - And related! Key responsibilities may include: - Develop novel, scalable algorithms and/or modeling techniques that advance the state-of-the-art in science areas such as robotics, artificial intelligence, or related - Tackle challenging, innovative research problems within robotics, artificial intelligence, or related that bridge the gap between theoretical advancement and practical robotics implementation - Collaborate with cross-functional teams to solve complex problems, and to communicate research findings clearly, leveraging your expertise in robotics, artificial intelligence, or related - Demonstrate the ability to work independently, thrive in a fast-paced, ever-changing environment - Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. - Critically review and validate outputs from AI tools and automated systems. About the team Explore Robotics Teams at Amazon At Amazon, robotics is where innovative research meets real-world deployment. There are many different specialized robotics teams, including the few highlighted below. Learn more about Robotics at Amazon here: https://www.amazon.science/research-areas/robotics Fulfillment Technologies and Robotics We build dynamic partnerships between people and intelligent machines. This intricate collaboration helps Amazon fulfill orders with unmatched accuracy. Working in symphony with our robotic technology, you’ll have the opportunity to extend your technical capabilities by working alongside some of the industry’s most advanced technologies. This includes our fleet of autonomous mobile robots, sophisticated control software, and technologies like language perception, machine learning, object recognition, and semantic understanding of commands. These technologies help us deliver an ever-improving customer and employee experience, as well as improve the safety of our facilities. Amazon Robotics Amazon Robotics is at the epicenter of robotics innovation. We design, test, and build mobile robots and mechatronic systems. Our offices, labs, test floors, and factories are close together, which means we can design the hardware, write the code, test, build, and watch it all come to life. Our areas of focus include advancements in autonomous mobility, AI, robotic management software, and much more. Fauna Fauna is focused on building capable, safe, and genuinely delightful robots for everyday life. Fauna’s goal is simple: make robots people actually want to live and interact with everyday. Fauna develops tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Fauna’s work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but overall experience. Amazon Fulfillment Technologies Amazon Fulfillment Technologies develops tech solutions that create network optimizations for all Amazon sites worldwide. We own network infrastructure and devices in all buildings and labor planning and scheduling technology for fulfillment centers in North America and Europe.
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Applied Scientist III Job Location: Seattle, Washington Job Number: AMZ10037670 Position Responsibilities: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. Position Requirements: Master’s degree or foreign equivalent degree in Computer Science, Machine Learning, Engineering, or a related field and two years of research or work experience in the job offered, or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation. Employer will accept a Bachelor’s degree or foreign equivalent degree in Computer Science, Machine Learning, Engineering, or a related field and five years of progressive post-baccalaureate research or work experience in the job offered or a related occupation as equivalent to the Master’s degree and two years of research or work experience. Must have one year of research or work experience in the following skill(s): (1) programming in Java, C++, Python, or equivalent programming language; and (2) conducting the analysis and development of various supervised and unsupervised machine learning models for moderately complex projects in business, science, or engineering. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. 40 hours / week, 8:00am-5:00pm, Salary Range $167,100/year to $226,100/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. #0000