Software Engineer (New Grad) - Machine Learning Platform Infrastructure

About Quora:

Quora’s mission is to grow the world's collective intelligence. They operate two platforms:

• Quora: A global knowledge-sharing platform with millions of monthly unique visitors, enabling people to share insights and learn from each other.

• Poe: A cloud workspace where users run multiple AI agents on shared context and tools, combining multiple frontier models with collaboration features.

Quora fosters a culture of transparency, idea-sharing, and experimentation, empowering teams to innovate and grow together. This role focuses on the Quora product.

About the Team and Role:

Machine Learning is central to Quora’s mission, with 100+ ML models in production powering various product features. These models range from linear models to deep neural networks and operate at massive scale, serving hundreds of millions of users monthly.

The team owns Quora’s ML platform and ranking infrastructure across four key areas: serving reliability, ML engineer enablement and developer velocity, business impact, and cost efficiency. The goal is to empower ML engineers to solve problems at scale.

As a Software Engineer (New Grad), you’ll work at the intersection of machine learning, distributed systems, and GPU serving performance, directly impacting Quora’s long-term success.

No prior ML infrastructure experience is required. You’ll learn from senior engineers, receive dedicated mentorship, and ship to production within your first few weeks.

Stack: Python, Go, C++, PyTorch, Kubernetes/EKS, NVIDIA Triton, Ray, AWS.

🚀 Check out the MLP team’s projects for examples of their work, including:

• 3x faster ML with 25% lower costs using 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:

• Help build and maintain the core infrastructure for Quora’s ML platform, ensuring high availability, scalability, and performance.

• Develop and optimize distributed systems for serving ML models in production, including Large Recommendation Models (LRMs) and Large Language Models (LLMs).

• Focus on GPU model serving, optimizing for latency, throughput, and cost to support larger and more capable models.

• Contribute to platform initiatives like PyTorch-first standardization and ML ecosystem modernization.

• Improve ML developer velocity by building tooling for model development, testing, and deployment.

• Modernize the feature store to enable faster production deployment of new features for ML engineers.

• Participate in on-call rotations to resolve production issues and grow ownership of the platform.

Minimum Requirements:

• Availability during Quora’s coordination hours (Mon-Fri: 9am-3pm Pacific Time).

• A 2025 or 2026 graduate with or pursuing a B.S., M.S., or Ph.D. in Computer Science, Engineering, or a related technical field.

• Genuine interest in large-scale distributed systems, infrastructure, and machine learning.

• Knowledge of Python, Go, or C++, or the ability to learn them quickly.

• A passion for learning and continuous improvement.

Preferred Requirements:

• Previous software engineering experience via internships, work, open-source contributions, or coding competitions.

• Coursework or hands-on experience with ML frameworks like PyTorch or TensorFlow.

• Exposure to Kubernetes, Docker, or cloud technologies like AWS.

• Experience with low-level performance work, such as profiling, benchmarking, or optimization.

• Passion for Quora’s mission and goals.

Quora values diversity and inclusivity and encourages applications from all backgrounds, especially those from underrepresented groups in tech.

Additional Information:

Applications are accepted on an ongoing basis. This role is a backfill for an existing vacancy.

Quora offers benefits including medical/dental/vision coverage, equity refreshers, remote work reimbursement, paid time off, and more.

Starting compensation varies based on factors like experience, location, education, and business needs.

• US candidates only: Salary range is $97,600 - $139,000 USD + equity + benefits.

• Canada candidates: Toronto/Vancouver: $125,320 - $142,783 CAD; Other locations: $116,965 - $133,264 CAD + equity + benefits.

• Equity-eligible countries offer a flexible equity program where a portion of compensation can be taken as cash.