Software Engineer, Machine Learning

What you'd actually do

1. Research, design, develop, and test operating systems-level software, compilers, and network distribution software for massive social data and prediction problems.
2. Have industry experience working on a range of classification 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.
3. Working on problems of moderate scope, develop highly scalable systems, algorithms and tools leveraging deep learning, data regression, and rules based models.
4. Suggest, collect, analyze and synthesize requirements and bottleneck in technology, systems, and tools.
5. 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.
6. Receiving general instruction from supervisor, code deliverables in tandem with the engineering team.
7. Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).

Skills

What the JD emphasized

• highly scalable systems
• state-of-the-art deep learning techniques
• highly-scalable performant solutions

Other signals

• develop highly scalable systems, algorithms and tools leveraging deep learning
• explore state-of-the-art deep learning techniques
• Adapt standard machine learning methods to best exploit modern parallel environments