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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 WhatsApp 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.
Job Responsibility:
Research, design, and develop machine learning systems for resource constrained environments such as mobile devices and AR/VR headsets
Utilize low level programming, algorithmic optimization, and hardware acceleration to optimize the performance of the PyTorch machine learning framework across mobile hardware environments
Apply deep neural networks to a variety of domains such as image object detection, image background segmentation, foundational language models, ads/feed/search ranking, and audio transcription to enrich user experience in Meta’s family of social media applications
Apply GPU programming to enable acceleration of machine learning models via the mass parallelism offered by graphics processors
Investigate and resolve performance bottlenecks in real-time machine learning workloads
Design and implement moderate-sized software modules of the Pytorch machine learning library with a focus on achieving good software quality
Apply GPU shader programming with Cuda, OpenCL, Vulkan and usage of CPU vector intrinsics to implement optimized mathematical functions that will be used to execute Pytorch machine learning models on mobile and edge (e.g. VR headsets) devices
Collaborate with team members
review code, delegate tasks, deliberate design reviews, etc.
Requirements:
Requires a Bachelor’s degree (or foreign degree equivalent) in Computer Science, Computer Software, Mechatronic Engineering, Applied Sciences, Mathematics, Physics, or related field
Requires completion of a university-level course, research project, internship or thesis in the following: Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
Machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems
Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark
Scripting languages: Perl, Python, PHP, or shell scripts C, C++, C#, or Java
Python, PHP, or Haskell
Relational databases and SQL
Linux, UNIX, or other *nix-like OS including file manipulation and simple commands
Distributed systems including sharding, consistency, and availability