Principal Software Engineer, Dash

Role Description

The Dropbox team is growing, and were looking for a technical leader to help shape the architecture and services that power one of Dropbox’s newest AI offerings, Dash. Our team culture rewards a bias for action, engineering partnership in defining the product, and discipline in how we develop. Youll thrive in our team if you love chasing impact, working through ambiguity, and creating a culture of innovation. Dash is one of the fastest growing products at Dropbox and we need an extraordinary technical leader to scale the great work we are already doing and to diversify our efforts to continue to grow the business. 

The work will involve defining the large scale, low latency architecture that supports multiple products which touch hundreds of millions of people and a *lot* of data. These services support product features like search, personalization and other solutions for fragmentation of Data. The architecture handles complex use cases of several ml-based features and large scale data ingestion, preprocessing, enrichment, and knowledge extraction, indexing, and Dash’s backend APIs.

Responsibilities

• You will work with infrastructure experts across Dropbox and Enterprise to help shape how we architect and build the foundation for multiple AI first products
• You will build delightful products and experiences for millions, while working alongside an excellent, multi-functional team across Engineering, Product and Design
• You will help craft the direction of building capabilities for Dropbox Dash

Requirements

• BS, MS, or PhD in Computer Science or related technical field involving large scale distributed systems, or equivalent technical experience
• 8+ years of experience building consumer or enterprise facing large scale distributed systems 
• Demonstrated skill developing low latency and fault tolerant architecture 
• Strong communication skills and a demonstrated passion for helping others leverage machine learning
• Proven software engineering skills across multiple languages