Staff Machine Learning Engineer, Quora

• If you have a passion for search and retrieval, emerging LLM modeling techniques and recommender system at scale, this role offers an invaluable opportunity to spearhead progress
• Ability to be available for meetings and impromptu communication during Quora's “coordination hours" (Mon-Fri: 9am-3pm Pacific Time)
• Demonstrated professional experience in software development and machine learning
• Proven track record of tech leading NLP/Search models to solve industry-scale problem
• 4+ years of experience writing Python or C++ code
• Previous experience tech leading complicated end to end machine learning systems
• Experience with leading large-scale multi-engineer projects
• BS, MS or PhD in Computer Science, Engineering or a related technical field

Desirable

• We are seeking a talented Staff ML Engineer to join us in building Poe, an exciting new platform at the forefront of AI
• You will work at the cutting edge of technology to develop the Poe’s bot eco system with state of art machine learning algorithms
• This includes architecting a scalable recommender system architecture, building performant and reliable NLP applications and collaborating with our product team to build various personalized AI-driven user features
• Poe has experienced awesome growth so far, and as a Staff MLE for Poe, you can build the future of bot ecosystem while gaining invaluable experience with complex problems
• Your contributions will have a direct influence on the Poe’s roadmap
• Be responsible for developing, designing, and maintaining cutting-edge applied LLM projects including but not limited to search result retrieval, recommender systems and multi-bot agents, ensuring smooth and engaging user experiences
• Participate in a wide variety of NLP activities, including refining and optimizing prompting, retrieval-augmented generation, etc to improve the outcome of LLM
• Continuously research and stay up-to-date with the latest developments in NLP and large language models, utilizing innovative techniques and methodologies to enhance our models
• Take end to end ownership of large and scalable machine learning systems - from data pipelines, feature engineering, candidate extraction, model training, as well as integration into our production systems