Machine Learning Engineer, Quora

• Availability for meetings and impromptu communication during Quora's “coordination hours" (Mon-Fri: 9am-3pm Pacific Time)
• A 2024, 2025 or 2026 graduate with or pursuing a B.S., M.S., or Ph.D. in Computer Science, Engineering or a related technical field
• Strong understanding of mathematical foundations of Machine Learning algorithms
• Experience of LLM applications or transformer models
• Knowledge of Python or C++, or the ability to learn them quickly
• Strong command of written English, with the ability to evaluate tone, accuracy, clarity, and nuance in written content
• A passion for learning and always improving yourself and the team around you
• Previous software engineering experience via an internship, work experience or coding competition

Desirable

• Our small engineering team works on challenging problems every day
• We have a culture that's rooted in constantly learning and improving, and our engineers are encouraged to think big and experiment with new ideas
• Using continuous deployment, we quickly see our changes in the product and make fast iterations
• Our engineers focus on creating polished products and writing high quality code by designing APIs and abstractions that are extensible and maintainable
• Everyone on the engineering team has a huge impact on our product and our company
• Join our team to build and improve consumer-facing AI systems at the core of Quora's product experience!
• As a Machine Learning Engineer, you'll develop high-quality AI-powered content experiences and drive meaningful engagement through scalable ML solutions
• This role emphasizes hands-on model development, experimentation, and productionization in close collaboration with cross-functional partners
• Build and iterate on consumer-facing AI features powered by large language models (LLMs) and related generative AI systems
• Collaborate with engineers across the AI engineering stack — prompt engineering, agentic workflow optimization, evaluation and more
• Run structured experiments (e.g., A/B tests, offline evaluations) to measure impact on engagement, quality, and trust metrics
• Partner closely with product and engineering to translate user needs into scalable AI-powered solutions
• Strengthen model reliability through monitoring, guardrails, quality analysis, and failure mode investigation
• Optimize the latency, cost, and scalability of production AI systems