Research Scientist, Machine Learning (Phd)

At Meta, we use Machine Learning across a diverse set of applications to help people discover better content, connect with things that matter most to them, keep our community safe from harmful content, and to build the future of connection within virtual and augmented reality. As an example, we strive to find ways to deliver more engaging content in News Feed, present the most relevant ads possible, and to build the best HW products and tools that help people feel connected, anytime, anywhere.In order to meet the demands of our scale, we approach Machine Learning challenges from a system engineering standpoint, both pushing the boundaries of scalable computing and tying together numerous complex platforms to build models that leverage trillions of actions, as well as building the best models under HW constraints. Our research and production implementations leverage many of the innovations being generated from Meta’s research in Distributed Computing, Artificial Intelligence, and Databases, and run on the same hardware and network specifications that are being open sourced through the Open Compute project. As a Research Scientist, you will help build Machine Learning systems and models behind Meta’s products, create web applications that reach millions of people, build high volume servers and be a part of a team that’s working to help connect people around the globe.

Research Scientist, Machine Learning (PhD) Responsibilities:

• Develop highly scalable classifiers and tools leveraging Machine Learning, regression, and rules-based models with a high degree of autonomy
• Suggest, collect and synthesize requirements and create effective feature roadmap
• Build strong crossfunctinal partnerships and 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) Actively seek and give feedback in alignment with Meta’s Performance Philosophy

Minimum Qualifications:

• Currently has, or is in the process of obtaining a PhD degree or completing a postdoctoral assignment in the field of Machine Learning, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta.
• Research and/or work experience in Machine Learning, deep learning, reinforcement learning, NLP, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, information retrieval or computer vision.
• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Experience in systems software or algorithms.
• Experience in at least one of the following: Java, C/C++, Perl, PhP, or Python Demonstrated software engineer experience via an internship, work experience, coding competitions, or used contributions in open source repositories (e.g. GitHub)

Preferred Qualifications:

• Proven track record of achieving results as demonstrated by grants, fellowships, patents, as well as first-authored publications at workshops or conferences such as ICML, NIPS, KDD or similar
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and diverse points of view to determine a path forward
• Experience with Hadoop/Hbase/Pig or Mapreduce/Swazall/Bigtable
• Experience working with ML Frameworks such as PyTorch, Spark ML or Tensorflow
• Experience working and communicating cross functionally in a team environment

About Meta:

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps 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. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.