Machine Learning Engineer (New Grad) - Quora

About Quora:

Quora’s mission is to grow the world's collective intelligence. To do so, we have two platforms:

• Quora: a global knowledge sharing platform with millions of monthly unique visitors, bringing people together to share insights on various topics and providing a unique platform to learn and connect with others.

• Poe: a cloud workspace where millions of users run multiple AI agents on shared context and tools. One subscription, every frontier model, and the collaboration layer that makes them work together.

Behind these products are passionate, collaborative, and high-performing global teams. We have a culture rooted in transparency, idea-sharing, and experimentation that allows us to celebrate success and grow together through meaningful work. Join us on this journey to create a positive impact and make a significant change in the world.

This role will be working on our Quora product.

About the Team and Role:

Our ML team is small and highly collaborative, working on challenging problems that impact hundreds of millions of Quora users. We encourage engineers to think big, experiment with new ideas, and continuously learn and improve. With over 100 ML models in production, engineers have opportunities to take ownership of meaningful projects, work closely with experienced teammates, and quickly see the impact of their work.

We're building the next generation of AI-powered systems to improve content quality and safety across Quora. By leveraging LLMs and agentic workflows, we're transforming how we detect harmful content, automate complex decisions, and respond to evolving challenges at scale. As a Machine Learning Engineer (New Grad), you'll work alongside experienced engineers to design, evaluate, and deploy these systems in production.

No previous trust and safety experience is required. You'll learn from the engineers who built these systems.

Stack: Python, PyTorch, TensorFlow, Spark, Kubernetes

Responsibilities:

• Design, build, and deploy LLM-powered agents to detect and address spam, harassment, and other policy-violating content at Quora's scale
• Develop agentic workflows to automate complex tasks, improving the efficiency and effectiveness of our content and user moderation systems
• Design and improve ML systems that adapt to evolving abuse patterns and new challenges
• Evaluate agent performance through offline evaluations, online experiments, and production monitoring, balancing accuracy, reliability, latency, and cost
• Investigate failure cases, experiment with new approaches, and iterate on solutions to improve system performance
• Identify new opportunities to apply ML and LLMs to content quality and safety across Quora
• Collaborate with experienced engineers and take ownership of projects from early development through production

Minimum Requirements:

• Availability for meetings and impromptu communication during Quora's “coordination hours” (Mon-Fri: 9am-3pm Pacific Time)

• B.S., M.S., or Ph.D. in Computer Science, Engineering, or a related technical field, completed or in progress
• Strong understanding of mathematical foundations of Machine Learning algorithms
• Coursework, research, or project experience with LLM applications or transformer models
• Strong knowledge of Python
• A passion for learning and always improving yourself and the team around you
• Able to start full-time in early 2027

Preferred Requirements:

• Previous software engineering experience via an internship, work experience, open-source contribution, or coding competition
• Hands-on experience building and evaluating LLM agents, in projects, research, internships, or production
• Regular use of AI coding tools such as Claude Code or Codex
• Coursework or projects in natural language processing (NLP)
• Genuine interest in content safety and quality
• Passion for Quora's mission and goals

At Quora, we value diversity and inclusivity and welcome individuals from all backgrounds, including marginalized or underrepresented groups in tech, to apply for our job openings. We encourage all candidates who share a passion for growing the world’s knowledge, even those who may not strictly meet all the preferred requirements, to apply.