Staff Infrastructure Software Engineer, ML Platform

Dropbox · Remote

Company

Dropbox

Location

Remote

Type

Full Time

Job Description

Role Description
As a Staff Software Engineer joining our Machine Learning platform team, you will shape the Machine Learning foundation for Dropbox. 
 
In this role, you will be crucial in architecting and developing reliable and performant software infrastructure that enables our customers to build high impact ML solutions at scale. You will work closely with machine learning engineers and data scientists to develop and maintain new systems and tooling, accelerating their ML development velocity and providing great and unified user experiences throughout the whole ML lifecycle.
 
We care deeply about collaboration, feedback, and iteration. Trust and respect are deeply rooted in our engineering culture. We're bold when it comes to shipping high-leverage projects, even if they're risky or novel. We hope you'll join us!
Responsibilities
  • Identify and lead strategic initiatives that streamline ML Operations (MLOps), optimize platform performance, and improve system health
  • Design, build, test and maintain scalable and reliable infrastructure that supports machine learning workflows from training to serving ML models
  • Provide technical leadership and guidance to engineers and multi-functional partners
  • Collaborate with cross-functional teams to understand their requirements and develop solutions that meet their needs
Requirements
  • BS, MS, or PhD in Computer Science or related technical field involving coding (e.g., physics or mathematics), or equivalent technical experience
  • 10+ years of professional software development experience 
  • Extensive experience building and owning large-scale, multi-threaded, geographically distributed backend systems
  • Highly skilled at developing and debugging in C/C++, Java, or Go, with knowledge of Python a plus
  • Strong communication skills and ability to work effectively in a collaborative team environment
  • Experience with ML infrastructure
  • Familiarity with relevant technology stacks a plus (ie. AWS, Kubernetes, Docker, Kubeflow, Ray, Tensorflow, PyTorch)
Apply Now

Date Posted

05/18/2023

Views

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