Machine Learning Engineering Manager
Posted on 3/31/2023

1,001-5,000 employees

Smart workspace & storage solutions
Company Overview
Dropbox’s mission is to design a more enlightened way of working with their custom-built storage infrastructure designed for a variety of needs. The company is committed to keeping life organized and keep work moving with their secure file sharing, collaboration, and storage solutions.
Remote • United States
Experience Level
Desired Skills
Natural Language Processing (NLP)
AI & Machine Learning
  • 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
  • 3+ years of people management experience with an ML engineering team
  • ML domain knowledge to set and execute technical strategy (in partnership with the Tech lead)
  • 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, Java or C/C++
  • Experience with Machine Learning software tools and libraries (e.g., Scikit-learn, TensorFlow, Keras, PyTorch, HuggingFace etc.)
  • Relevant experience can range from working on a wide-variety of optimization, and classification problems, e.g. recommender systems, natural language processing, text/sentiment classification, click-through rate prediction, collaborative filtering/recommendation, or spam detection
  • Excellent verbal and written communication skills
  • Understand what matters most and prioritize effectively
  • Leading and managing a team of machine learning engineers in the development of cutting edge ML models, with a focus on recommender systems and language models
  • Understand the ML stack at Dropbox, and build systems that help Dropbox personalize their users' experience
  • Collaborating with product and design teams to understand the requirements and constraints of the project and to ensure the models developed meet those requirements
  • Mentoring and guiding senior and junior engineers in the development of their skills and knowledge
  • Providing technical leadership and guidance to the team in the development, implementation, iteration, and deployment of machine learning models
  • Set direction for the team, anticipate strategic and scaling-related challenges via thoughtful long-term planning
  • Collaborating with other engineering teams to ensure that models are properly integrated into the larger product and shipped to production