Job Description
DESCRIPTION
Are you excited about developing state of the art Machine Learning (ML) capabilities to power Amazon's robotic storage solutions? Are you looking for opportunities to build and deploy Machine Learning models on real problems at truly vast scale? At Amazon Robotics we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience - at Amazon scale.
The Robotic Storage Technologies (RST) team at Amazon Robotics is seeking a passionate, collaborative, hands-on Applied Scientist to develop algorithms that foster optimal choice selection in Amazon's next generation robotic storage systems. You will explore and leverage huge amounts of inventory and robotic transactional data from our growing network of warehouses across the globe. You will utilize a combination of machine learning, reinforcement learning, and optimization methods to drive high performance that scales and adapts under dynamic operating conditions. This work spans from early research and simulation of new ideas through deployment and continuous improvements as our solutions and Amazon's needs continually evolve.
The ideal candidate for this position will be familiar with planning or learning algorithms at both the theoretical and implementation levels. You will have the chance to solve complex scientific problems and see your solutions come to life in Amazon’s warehouses!
Key job responsibilities
- Research new ideas and contribute to the vision of our science roadmap.
- Design and develop models and algorithms that perform at Amazon scale.
- Work closely with software engineering teams to deploy your innovations.
- Monitor, troubleshoot, and continuously improve production in performance.
- Contribute to development of simulation and other forms of offline policy evolution.
- Publish your work at major conferences/journals.
A day in the life
On a typical day in this role you will work to progress your research projects; meet with robotic storage engineering, systems, and solutions stakeholders; brainstorm with other scientists on the team and participate in team processes. You will follow your research projects though the entire life cycle of design, prototype implementation, evaluation, analysis, and you will communicate your findings and results through technical papers and reports. You will collaborate with software development teams to implement your ideas in production software services and design experiments to pilot your algorithms and models in Amazon's robotic warehouses.
Amazon offers a full range of benefits that support you and eligible family members, including domestic partners. 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!
About the team
Robotic Storage Technologies (RST) owns automation of storage and retrieval solutions in Amazon's vast network of fulfillment centers across the globe. Our solutions are driven by fleets of hundreds of robots, and our customers are operations engineers and associates who help fulfill millions of Amazon.com orders per day. You will be part of a team of scientists embedded within the software organization that supports the solutions you help to deploy.
BASIC QUALIFICATIONS
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- 3+ years of building machine learning models or developing algorithms for business application experience
- Experience applying theoretical models in an applied environment
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
Job Tags
Full time, Temporary work, Seasonal work,