Software Engineer, Machine Learning - Menlo Park, CA | Remote, US
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
Individual pay is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base salary, Meta offers benefits. Learn more about benefits at Meta. Apply
- Research, design, develop, and test operating systems-level software, compilers, and network distribution software for massive social data and prediction problems.
- Have industry
experience working on a range of ranking, classification, recommendation, and optimization problems, such as payment fraud, click-through or conversion rate prediction, click-fraud
detection, ads/feed/search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detection. - Working on problems of moderate scope, 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
technology, systems, and tools. - Develop solutions that iterate orders of magnitude with a higher efficiency, efficiently leverage orders of magnitude and more data, and explore state-of-theart deep learning techniques.
- Receiving general instruction from supervisor, code deliverables in tandem with the engineering team.
- Adapt standard machine learning methods to best
exploit modern parallel environments (such as distributed clusters, multicore SMP, and GPU). - Telecommute from anywhere in the US permitted.
- Requires a Master’s degree in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field. Requires completion of a university-level course, research project, internship, or thesis in the following:
- 1. Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
- 2. Machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems
- 3. Translating insights into business recommendations
- 4. Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark
- 5. Developing and debugging in C, C++, and Java
- 6. Scripting languages: Perl, Python, PHP, or shell scripts
- 7. C, C++, C#, or Java
- 8. Python, PHP, or Haskell
- 9. Relational databases and SQL
- 10. Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, or Perforce)
- 11. Linux, UNIX, or other *nix-like OS as evidenced by file manipulation, advanced commands, and shell scripting
- 12. Build highly-scalable performant solutions
- 13. Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction
- 14. 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 - 15. Distributed systems.
Individual pay is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base salary, Meta offers benefits. Learn more about benefits at Meta. Apply
Job Profile
Regions Countries Benefits/Perks SkillsAlgorithms Bigtable C C++ Computer graphics Computer Science Data Mining Data processing Deep Learning Distributed Systems Emacs Git Hadoop Haskell HBase Java Linux Machine Learning MapReduce MXNet Networking Optimization Perforce Perl PHP Pig Python PyTorch Relational databases Sawzall Shell scripting Spark SQL Subversion TensorFlow UNIX VIM
Tasks- Adapt standard machine learning methods to exploit modern parallel environments
- Code deliverables in tandem with the engineering team
- Develop highly scalable systems, algorithms, and tools leveraging deep learning
- Research, design, develop, and test operating systems-level software
- Work on ranking, classification, recommendation, and optimization problems
Applied Sciences Computer Engineering Computer Science Computer Software Engineering Master’s Degree in Computer Science Mathematics Physics Related Field
RestrictionsTelecommute from anywhere in the U.S. permitted
TimezonesAmerica/Anchorage America/Chicago America/Denver America/Los_Angeles America/New_York Pacific/Honolulu UTC-10 UTC-5 UTC-6 UTC-7 UTC-8 UTC-9