Company: |
Meta |
Location: |
Menlo Park, CA, USA |
Type: |
Full-Time |
Salary: |
$214032 - $240240 |
Requirements
- Requires a Master’s degree(or foreign equivalent degree) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field and three years of work experience in the job offered or in a computer-related occupation
- Requires three years of experience in the following:
- 1. Machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems
- 2. Translating insights into business recommendations
- 3. Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, or Perforce)
- 4. Linux, UNIX, or other *nix-like OS as evidenced by file manipulation, advanced commands, and shell scripting
- 5. Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction
- 6. Distributed systems
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, e.g. 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 (e.g. distributed clusters, multicore SMP, and GPU).
Preferred Qualifications
Benefits
- No benefits info provided.
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