Senior Software Engineer, Quora

• Availability for meetings and impromptu communication during Quora's “coordination hours" (Mon-Fri: 9am-3pm Pacific Time)
• Experience with building and owning end-to-end machine learning or data science-related systems
• Experience instrumenting ML workloads for performance monitoring/efficiency
• Experience with high performance, large scaled distributed systems
• 4+ years of industry experience in Machine Learning, Infrastructure or related fields
• 4+ years of experience writing production code in Python, C++, or similar language
• BS or MS in Computer Science, Engineering or a related technical field

Desirable

• Machine Learning plays an important role in helping Quora further its mission of growing and sharing the world's knowledge
• We have 100+ Machine Learning models in production powering various product features
• We use a variety of algorithms — everything from linear models to decision trees and deep neural networks
• Our production models operate at a huge scale and help over a hundred million people using Quora every month
• We want to empower all ML engineers at Quora to be as impactful as they can be in solving different ML problems at scale
• To that end, we are looking for engineers to help us build our company-wide ML development platform
• In this role, you will be the part of a small team solving very interesting technical problems at the intersection of exciting domains like Machine Learning, Distributed Systems and High Performance Computing
• Your work will have an enormous impact on Quora's long-term success
• This role will focus primarily on ML infrastructure and Distributed System (80%), with some involvement in supporting model deployment (20%)
• Design, develop, and maintain the core infrastructure that powers Quora's machine learning platform, ensuring high availability, scalability, and performance
• Build scalable and reliable distributed systems for serving machine learning models
• Optimize infrastructure performance across the ML platform, identifying and resolving bottlenecks to meet the demands of large-scale machine learning workloads
• Collaborate with machine learning engineers to understand their infrastructure needs and provide solutions that enable them to build and deploy models efficiently
• Contribute to the design and implementation of our next-generation machine learning infrastructure, focusing on scalability, reliability, and cost-effectiveness
• Develop services on top of open source technologies like Kubernetes, Tensorflow, and PyTorch
• Own business-critical infrastructure, help resolve production issues, and participate in the team-wide on-call rotation
• Collaborate with ML engineers who use the platform, and help them be more impactful