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Software Engineer – Machine Learning
| Company | Meta |
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| Location | Bellevue, WA, USA |
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| Salary | $178052 – $200200 |
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| Type | Full-Time |
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| Degrees | Bachelor’s |
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| Experience Level | Junior, Mid Level |
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Requirements
- Requires a Bachelor’s degree (or foreign degree equivalent) 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. Developing and debugging in C, C++, and Java
- 4. Scripting languages: Perl, Python, PHP, or shell scripts
- 5. C, C++, C#, or Java
- 6. Python, PHP, or Haskell
- 7. Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, or Perforce)
- 8. Linux, UNIX, or other *nix-like OS including file manipulation and simple commands
- 9. Data structures and algorithms.
Responsibilities
- 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, eg 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-the-art 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 (eg distributed clusters, multicore SMP, and GPU).
Preferred Qualifications
No preferred qualifications provided.
