Job Description
Designing, implementing and analyzing from end to end – novel, state of the art Machine/Deep Learning based applications under a dedicated H/W environment. The job includes understanding of machine and deep learning models to the level of being able to break it down functionally and map it on a groundbreaking H/W architecture.
Key Responsibilities
  • Design and develop Machine/Deep Learning models for applications in a variety of fields ranging from Big Data, Computer Vision, Natural Language Processing, Graph Analysis to Recommendation Systems
  • Design and develop large scale training and deployment systems, using multiple GPU's and servers, dedicated H/W, Multi-threading and Parallelization
  • Select appropriate datasets and data representation methods, use and develop tools for manipulating, loading and visualizing data in different data structures
  • Run Machine Learning tests and experiments, manage and analyze results
  • Work closely with PhD researchers, turn abstract concepts into working products
  • Develop advanced modeling capabilities under dedicated H/W environment and identify key strengths in H/W for said models and algorithms
  • Be an active participant in the team's research on emerging ML/Deep Learning/Data Science solutions and be conversant with the latest developments in the field
  • Prepare reports and presentations for internal and external purposes, and as applicable, jointly co-author publications in peer-reviewed journals and conferences
Requirements:
  • 3+ years experience and deep understanding of machine learning/deep learning, neural networks, statistical classification, and related techniques with their underlying theory and math
  • Practical, hands on, experience in architecting, training and analyzing deep learning models (Convolutional/Recurrent Networks) for image and/or text on large scale data sets
  • Proficient in Python, C/C++, Tensorflow or other Deep Learning frameworks, under Linux environment
  • Experience with additional Machine Learning packages and infrastructures such as scikit-learn, pandas, spark, Kubernetes etc.
  • Additional experience with multiple GPU programming for training deep learning models, and cloud environments such as AWS, Azure is desirable
  • Full stack experience in big data processing, aggregation, analysis and visualization
  • Team player with strong communication skills
  • BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus