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Design and deploy large-scale machine learning systems at Dropbox to enhance user collaboration and organization. Work with cross-functional teams to integrate AI/ML advancements into products, utilizing tools like PyTorch and Scikit-learn.
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Dropbox
Enhance the Dropbox Android app by leading feature development, mentoring team members, and ensuring top-notch engineering practices.
Dropbox
Enhance the Android experience at Dropbox by leading feature development, solving complex problems, and mentoring a team of engineers.
Dropbox
As a Senior Machine Learning Engineer at Dropbox, you will leverage cutting-edge AI/ML technologies to design, build, deploy, and refine large-scale machine learning systems. Your work will power features like universal AI search and AI-assisted organization in Dropbox Dash, enhancing how millions of users collaborate and stay organized. You'll collaborate with Product, Design, Infrastructure, and Frontend teams, contribute to the technical strategy for the end-to-end ML lifecycle, and explore advancements in Search, LLMs, Recommender Systems, etc. The role requires a BS/MS/PhD in Computer Science or related field, 8+ years of engineering experience with 5+ years in ML/AI systems, strong software engineering skills in Python, Go, C/C++, and familiarity with ML libraries like PyTorch.
As a Senior Machine Learning Engineer, you will play a key role in advancing Dropbox’s mission to create a more enlightened way of working. Leveraging cutting-edge AI/ML technologies, you will design, build, deploy, and refine large-scale machine learning systems. Your work will power Dropbox Dash’s universal AI search and AI-assisted organization features, transforming how millions of Dropbox users collaborate, stay organized, and focus on the work that truly matters.
Our Engineering Career Framework is viewable by anyone outside the company and describes what’s expected for our engineers at each of our career levels. Check out our blog post on this topic and more here.
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. Develop and maintain production-quality code for serving machine learning models at scale
Work with Product, Design, Infrastructure and Frontend teams to bring your models, and features to life
Contribute to team’s technical strategy for the end-to-end machine learning lifecycle, ensuring alignment with business objectives and driving impactful outcomes
Explore and integrate the latest advancements in Search, LLMs, Recommender Systems, and Representation Learning into Dropbox's products
Many teams at Dropbox run Services with on-call rotations, which entails being available for calls during both core and non-core business hours. If a team has an on-call rotation, all engineers on the team are expected to participate in the rotation as part of their employment. Applicants are encouraged to ask for more details of the rotations to which the applicant is applying.
BS, MS, or PhD in Computer Science, Mathematics, Statistics, or other quantitative fields or related work experience
8+ years of experience in engineering with 5+ years of experience building Machine Learning or AI systems
Professional working experience in ML modeling for at least one of the following: Recommender Systems, Search, or Ranking
Strong industry experience working with large scale data
Strong collaboration, analytical and problem-solving skills
Familiarity with the state-of-the-art in Large Language Models
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., PyTorch, Scikit-learn, numpy, pandas, etc.)
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, Recommender Systems, Learning to Rank, Speech Processing, Learning from Semi-structured Data, Graph Learning, Large Language Models, and Retrieval-Augmented Generation
Experience building 0→1 ML products at large (Dropbox-level) scale or multiple 0→1 products at smaller scale including experience with large-scale product systems
