Senior Machine Learning Software Engineer

2 months ago

San Francisco, California, United States Dropbox Full time

Role Description

Dropbox is looking for a Machine Learning Engineer to join our User Understanding team. User Understanding team's mission is "to personalize users' journey through Dropbox products and features". The team develops models, systems and features that leverage the massive scale of Dropbox's user base to understand and predict user behavior to optimize the experience at all stages of the user journey at Dropbox. Relevant experience can range from working on a wide-variety of optimization, and classification problems, e.g. segmentation, propensity modeling, text/sentiment classification, click-through rate prediction, collaborative filtering/recommendation, or spam detection.

Responsibilities
  • Design, build, evaluate, deploy and iterate on large scale Machine Learning systems
  • Understand the Machine Learning stack at Dropbox, and build systems that help Dropbox personalize their users' experience
  • Work with Product, Design, Infra and Frontend teams to bring your models, and features to life
  • Work with large scale data systems, and infrastructure
  • Evaluate the performance of machine learning systems against business objectives
Requirements
  • BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
  • 5+ years of experience building Machine Learning or AI systems
  • Strong industry experience working with large scale data
  • Strong analytical and problem-solving skills
  • Proven software engineering skills across multiple languages including but not limited to Python, Go, C/C++
  • Experience with Machine Learning software tools and libraries (e.g., Scikit-learn, TensorFlow, Keras, PyTorch, etc.)
Preferred Qualifications
  • PhD in Computer Science or related field with research in machine learning
  • Experience with one or more of the following: natural language processing, deep learning, bayesian reasoning, recommendation systems, learning for search, speech processing, learning from semistructured data, graph learning, reinforcement or active learning, ML software systems, machine learning on mobile devices

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