Machine Learning Infrastructure Engineer
Employment Type: Full-Time
Facebook is seeking Machine Learning Infrastructure Engineers to join our engineering team. The ideal candidate will have industry experience working on a range of classification and optimization problems, e.g. payment fraud, click-through or conversion rate prediction, click-fraud detection, ads/feed/search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detection. The position will involve taking these skills and applying them to some of the most exciting and massive social data and prediction problems that exist on the web.MACHINE LEARNING INFRASTRUCTURE ENGINEER RESPONSIBILITIES Develop highly scalable systems, algorithms and tools leveraging deep learning, data regression, and rules based models Suggest, collect, analyze and synthesize requirements and bottleneck in the technology, systems and tools used by machine learning engineers and develop solutions that make machine learning engineers iterate orders of magnitude higher efficiency, leverage orders of magnitude more amount of data efficiently and explore state-of-the-art deep learning techniques Code deliverables in tandem with the engineering team Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)
MINIMUM QUALIFICATIONS MS degree in Computer Science or related quantitative field or Ph.D. degree in Computer Science or related quantitative field 3+ years of experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining or artificial intelligence Proven experience to translate insights into business recommendations Experience with Hadoop/HBase/Pig or MapReduce/Sawzall/Bigtable/Spark Knowledge developing and debugging in C/C++ and Java Experience with scripting languages such as Perl, Python, PHP, and shell scripts
PREFERRED QUALIFICATIONS Experience with Machine Learning Framework(s): (e.g. PyTorch, MXNet, Tensorflow) Experience with filesystems, server architectures, and distributed systems* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.
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