We are looking for a QA Engineer to work with our Matlantis development team.
Matlantis is a cloud service that supports materials discovery. Users perform various material simulations using inference results from PFN's proprietary general-purpose neural network potential called PFP.
Our team does not perform manual verification by QA staff, but instead performs automated E2E testing using tools. We believe that automated testing is the best way to maintain a high delivery frequency. The tests are created and maintained by the development team, but as the system changes and functions are added, the workload gradually increases, and we are finding it difficult to maintain quality and execute tests efficiently. As the system grows, it will become increasingly difficult to maintain delivery speed and provide value in a stable quality.
The Matlantis development team is looking for a QA engineer who wants to proactively work on solving these issues. We believe it is important to continue to deliver values to our customers quickly. While our goal is to create a development environment that delivers both speed and quality, we need to make appropriate judgments about the values to be provided and quality risks during daily development, and select and implement the best measures at that time. We are looking forward to working with engineers on the development team who can utilize their expertise in testing and verification in these activities.
This position is intended to be assigned to the Matlantis development team. Typical tasks on this team are listed below. We are looking for applicants who are able to think through a testing strategy and build and execute it on their own.
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Preferred Experience and Skills
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Preferred Networks (PFN) was established in March 2014 with the goal to develop practical, real-world applications of deep learning, robotics and other advanced technologies. PFN’s business domains include transportation, manufacturing, life sciences, robots, plant optimization, materials discovery, education and entertainment. In 2015, PFN developed Chainer™, the open-source deep learning framework. PFN’s MN-3 supercomputer, which is equipped with the MN-Core™ deep learning processor, topped the Green500 list three times in 2020 and 2021.
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