Machine Learning Engineer
New York, New York
Employer: Meta
Industry:
Salary: Competitive
Job type: Full-Time
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.
Machine Learning Engineer Responsibilities
Minimum Qualifications
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Machine Learning Engineer Responsibilities
- Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules based models.
- Suggest, collect and synthesize requirements and create effective feature roadmap.
- 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)
- work on a range of classification and optimization problems, e.g. payment fraud, click-through rate prediction, click-fraud detection, search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detention.
Minimum Qualifications
- Requires a Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or related field and two years of work experience in the job offered or in a computer-related occupation. Requires two years of experience in the following:
- 1. Insights into business recommendations
- 2. Hadoop/Hbase/Pig, Spark, or Mapreduce/Sawzall/Bigtable
- 3. Developing and debugging in C/C++ and Java
- 4. Scripting languages: Perl, Python, PHP or shell scripts
- 5. Deep learning framework such as PyTorch and Tensorflow
- 6. Developing machine learning algorithms for natural language processing applications
- 7. Large-scale distributed training and real-time inference
- 8. Building end-to-end machine learning pipelines for offline and online experiments.
Start preparing
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Created: 2024-04-24
Reference: 308752982005801
Country: United States
State: New York
City: New York
ZIP: 10036
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