- Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).
- Develop highly scalable classifiers, ranking models, regression systems, or tools to support recommendation and personalization use cases through leveraging machine learning, data regression, and rules based models.
- Suggest, collect and synthesize requirements and create effective feature roadmaps.
- Collaborate closely with the engineering team to deliver high-quality, production-ready code.
- Promote ML best practices by monitoring and analyzing model performance via experiments and offline evaluations, and contribute ideas for continuous improvements.
- Bachelor’s degree (or foreign degree equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field
- Experience must include completion of one coursework, research project, or internship involving the following:
- Developing and debugging in C, C++, and Java
- Scripting languages such as Python,Perl, or shell scripts
- SQL for data analysis and model evaluation
- Relational databases, data manipulation, and analysis using tools such as NumPy, and Pandas
- Developing and training models using PyTorch or equivalent modern deep learning frameworks
- Applying deep learning models in focus areas such as recommendation systems,NLP, computer vision, or multi modal learning
- Demonstrated ability to build and test models, conduct experiments, and interpret results in a collaborative, cross-functional environment involving engineering, research, and data teams
- Strong computer science fundamentals, with knowledge of distributed systems,computer architecture, network programming, operating systems, and low-level performance optimization
- Strong mathematical foundation in probability theory, linear algebra, multivariable calculus, stochastic processes, and optimization methods used in machine learning
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