We hire and develop collaborative subject matter experts in AI with a focus on computer vision, deep learning, semi-supervised and unsupervised learning. We target high-impact algorithmic unlocks in areas such as scene and activity understanding, large scale generative models, closed-loop control, robotic grasping and manipulation—all of which have high-value impact for our current and future fulfillment networks.
We are seeking a hands-on, seasoned Applied Scientists who will be deep in code and algorithms; who are technically strong in building scalable vision systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation. As a Applied Scientist, you will contribute to the research and development of advanced robotic systems; your work along with other top-notch scientists and engineers will deliver the world's most scalable and robust robotic systems. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and active learning.
As a Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on challenging customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a collaborative team environment.
You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a fearless disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers.
Key job responsibilities
Architect, design, and implement Machine Learning models for vision systems on robotic platforms
Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap.
Work with stakeholders across , science, and operations teams to iterate on design and implementation.
Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement.
Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment
Work directly with customers and partners to test prototypes and incorporate feedback
Mentor other engineer team members.
A day in the life
Amazon offers a full range of benefits for 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!
BASIC QUALIFICATIONS
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
PREFERRED QUALIFICATIONS
- Extensive experience writing scientific papers, with multiple publications in top machine learning, computer vision or NLP conferences/journals, like CVPR, ICCV, ECCV, PAMI, NeurIPS, ICML, ICLR, JMLR, etc.
- Expertise in generative models, 3D vision, transformers, or other computer vision, LLMs or machine learning techniques.
- Experience in large data analysis.
- Solid knowledge of statistics and probability.
- Experience working in a fast paced, highly collaborative, dynamic work environment.
- Ability to communicate effectively across multiple organizations in the company.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. 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, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.