Job Description: Machine Learning Engineer (ML Platform)

Employment Type: Full-Time Location Type: Remote Salary: [Not specified]

About Quora: Quora is a privately held, "remote-first" company with the mission to grow and share the world's knowledge. We operate two key products:

• Quora: A global knowledge sharing platform with over 400 million monthly unique visitors.
• Poe: A platform enabling users to chat with and explore various AI language models.

Our culture is rooted in transparency, idea-sharing, and experimentation, fostering a collaborative environment where teams celebrate success and grow together.

About the Team and Role: Machine Learning is integral to Quora's mission, with over 100 ML models in production powering product features. We are seeking engineers to help build our company-wide ML development platform, tackling complex technical challenges at the intersection of Machine Learning, Distributed Systems, and High-Performance Computing. This role will primarily focus on ML infrastructure and Distributed Systems (80%), with some involvement in supporting model deployment (20%).

🚀 See our MLP team's work in action:

• Quora: 3x Faster Machine Learning, 25% Lower Costs with NVIDIA Triton on Amazon EKS
• Building a Service Mesh in a Hybrid Environment
• Building Embedding Search at Quora
• Feature Engineering at Quora with Alchemy

Responsibilities:

• Design, develop, and maintain the core infrastructure for 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 for large-scale ML workloads.
• Collaborate with ML engineers to understand their infrastructure needs and provide efficient solutions for model building and deployment.
• Contribute to the design and implementation of our next-generation ML 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, resolve production issues, and participate in the team-wide on-call rotation.
• Collaborate with ML engineers who use the platform to enhance their impact.

Minimum Requirements:

• Availability for meetings and [Information truncated - please provide full details]