Research Engineer

Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps and services like Messenger, Instagram, and Whats App further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology.

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Research Engineer Responsibilities

• Develop highly scalable computer technology, such as algorithms, based on state-of-the-art areas of computer science, such as machine learning and neural network methodologies.
• Combine broad and deep knowledge of relevant software and technology research domains along with the ability to synthesize a wide range of technical requirements to make significant contributions to Meta's feature roadmap for its cutting edge platforms.
• Adapt Meta technology, such as machine learning and neural network algorithms and architectures, to best exploit modem parallel environments (e.g., distributed clusters, multicore SMP, and GPU).
• Collaborate with Research Scientists to facilitate research that enables learning the semantics of data (images, video, text, audio, and other modalities).
• Research, design and develop new algorithms and techniques to improve the efficiency and performance of Meta's platforms.
• Identify potential improvements in company's products and research and present effects of current engineering efforts on Meta's market standing.
• Apply knowledge of relevant research domains along with expert coding skills to platform and framework development projects.

Minimum Qualifications

Requires a Master's degree in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Analytics, Physics, or related field and four years of work experience in the job offered or in a computer-related occupation. Requires four years of experience in the following:

• Developing and debugging in C, C++, Java, Scala, or Python
• Performing research, developing an experiment, or designing a prototype in machine learning
• Machine learning and optimization
• Building systems based on machine learning or deep learning methods
• Using frameworks like PyTorch, Caffee2, Tensorflow, Theano, Keras, or Chainer
• Storage systems, distributed systems, HPC, compilers, CUDA programming, file systems, or server architectures
• Parallel modem environments (distributed clusters, multicore SMP, or GPU).