DOCOMO Innovations, Inc. (DII) is a subsidiary of NTT DOCOMO, Inc. in Japan, a world leader in mobile operations and a growing provider of comprehensive mobile services.
Open Services Innovation Group (OSIG) develops services using state-of-the-art technologies. We are using approaches from Machine Learning, Artificial Intelligence to build next-generation intelligent systems and services for data analysis, text analytics, machine translation and other problems.
We are looking for a Principal/Senior Software Engineer to help us improve machine learning and deep learning capabilities. The position is based in our Palo Alto office and reports directly to Vice President of OSIG.
- Explore and evaluate state-of-the art systems and architectures for machine learning and deep learning.
- Propose and implement system design and architecture to solve specific ML/DL problems in in-house or cloud environment.
- Communicate findings to team members. Work with researchers and scientists to understand problem requirements and propose adequate solutions. Continually strive to improve.
- Develop software packages, prototypes and workflows. Demonstrate leadership through concrete action and result-oriented approach. Actively participate in planning, updates on ongoing project progress and deliverables with other members of OSIG and DOCOMO team.
Skills and Experience
- B. S. (M. S. or Ph. D. preferred) in Computer Science or Engineering
- 5+ years of experience in engineering software for machine learning or deep learning
- Experience with acceleration hardware and software stacks such as CUDA, OpenCL, FPGA
- Experience with parallel and distributed systems and architectures such as Spark, Hadoop or MPI
- Experience with deep learning packages like Torch, TensorFlow, MxNet and similar
- Experience with running and deploying parallel and distributed systems on the cloud such as AWS, Rescale, Cycle Computing, and similar
- Design and implementation skills in C++. Familiarity with agile software development methodology and QA processes.
- Excellent communication and presentation skills
- Experience with practical deep learning development in the areas of natural language understanding, machine translation or similar
- Experience building deep learning architectures such as GPU computing clusters
- Experience in scaling out to very large data sets (100 of millions of training samples) and very deep networks (tens to hundreds of layers) both in training and deployment stage
- Experience with multiple deep learning architectures, such as backpropagation, convolutional and recurrent neural networks, long-short term memory networks, generative adversarial networks