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. To apply, click "Apply to Job" online on this web page.

Software Engineer, Machine Learning Responsibilities

• Help build the next generation of systems behind Meta's products, create web and/or mobile applications that reach over two billion people, and build high-volume servers to support content.


• Harness passion for technology and graduate-level knowledge, and help build highly-scalable performant solutions.


• Make use of graduate-level knowledge of algorithms and core computer science concepts and ability to apply knowledge to real world systems.


• Without direct assistance, make the most of ability to recognize and match patterns from different areas of computer science in production systems and hone skill in making architectural decisions.


• Employ deep knowledge in at least one area: data processing, programming languages, databases, networking, operating systems, computer graphics, or human computer interaction.

Minimum Qualifications

• Employer will accept a Master's degree in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field and 24 months of experience in the job offered or in a computer-related occupation. Experience must include 2 years of experience in the following:


• 1. C, C++, C#, or Java


• 2. Python, PHP, or Haskell


• 3. Relational databases and SQL


• 4. Linux, UNIX, or other *nix-like OS as evidenced by file manipulation, advanced commands, and shell scripting


• 5. Core web technologies: HTML, CSS, or JavaScript


• 6. Building highly-scalable performant solutions


• 7. Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction


• 8. Applying algorithms and core computer science concepts to real world systems as evidenced by recognizing and matching patterns from different areas of computer science in production systems


• 9. Distributed systems.

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