What are the responsibilities and job description for the Machine Learning Engineer (W2 ONLY) position at Convergenz?
Job Details
Summary:
We re looking for someone with Machine Learning Engineering experience to join a high performing team. Working with one of the most complex cloud platforms. Work will be concentrated on applying ML models for content moderation (text, video, audio). If this is you, we re excited to hear from you!
Responsibilities:
Participate in building large-scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc
Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently.
Design, develop, evaluate and iterate on predictive models for candidate generation and ranking(eg. Click Through Rate and Conversion Rate prediction) , including, but not limited to building real-time data pipelines, feature engineering, model optimization and innovation.
Design and build supporting/debugging tools as needed.
Minimum Qualifications:
Bachelor's degree or higher in Computer Science or related fields.
Strong programming and problem-solving ability.
Experience in applied machine learning, familiar with one or more of the algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, Wide and Deep etc.
Experience in Deep Learning Tools such as tensorflow/pytorch.
Experience with at least one programming language like C /Python or equivalent. Preferred
Qualifications:
Experience in recommendation system, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields.
Publications at KDD, NeurlPS, WWW, SIGIR, WSDM, ICML, IJCAI, AAAI, RECSYS and related conferences/journals, or experience in data mining/machine learning competitions such as Kaggle/KDD-cup etc.
