Staff Machine Learning Engineer, Recommendation Systems - Quora (Remote)

Quora is a privately held, "remote-first" company
.
This position can be performed remotely from multiple countries around the world. Quora’s mission is to grow the worlds 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. At Quora, machine learning drives nearly every aspect of our product, and our lean, high-velocity engineering team tackles complex, high-impact challenges daily. We thrive in a remote-first culture built on absolute autonomy, continuous learning, and bold experimentation.
We are looking for a Staff Machine Learning Engineer to define the technical future of Quora’s recommendation ecosystem. In this role, you will own the technical direction for feed ranking and retrieval, lead our highest-impact modeling bets, and pioneer the integration of LLMs and generative models to redefine how we understand and rank content. Beyond the home feed, you will also scale recommendation surfaces like notifications and digest emails, while architecting the underlying tools and abstractions that empower our entire engineering organization.
You will write production code, mentor rising talent, and actively shape how our team leverages AI-assisted development to accelerate our shipping velocity.
Lead the highest-impact projects across the recommender stack, from candidate generation and retrieval to ranking, re-ranking, and multi-objective optimization
Drive measurable improvements in user engagement and content quality through well-designed online experiments
Take end-to-end ownership of ML systems, including data pipelines, candidate extraction, feature engineering, model training, and production integration
Identify new opportunities to apply machine learning to different parts of the Quora product
Partner with product, data science, and infrastructure teams to align on priorities and tradeoffs
Mentor engineers and lead by example, raising the bar for code quality, modeling rigor and AI assisted dev velocity
6+ years of professional software development experience in machine learning
4+ years of professional experience building and optimizing Home Feed recommendation systems for social media and user-generated content platforms.
Deep knowledge of the full recommender stack: candidate generation, retrieval, ranking, re-ranking, embeddings, and multi-objective optimization.
Deep understanding of mathematical foundations of machine learning algorithms
Experience leading large-scale ML projects end-to-end with measurable outcome
BS, MS, or PhD in Computer Science
6+ years of experience writing Python or C++ code
Flexible and positive team player
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 are accepting applications on an ongoing basis. Quora offers a wide range of benefits including medical/dental/vision coverage, equity refreshers, remote work reimbursement, paid time off, employee assistance programs, and more. For US based applicants, the salary range is $219,989 - $320,613 USD + equity + benefits.
For Toronto and Vancouver based applicants, the salary range is $282,469 - $329,337 CAD + equity + benefits. For all other locations in Canada, the salary range is $263,638 - $307,381 CAD + equity + benefits.
In equity-eligible countries, we currently also offer a flexible equity program where a portion of equity compensation may be taken as cash.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
AI technology may assist in sorting applications and recording interview notes, but all decisions are made by a member of our team.
LI-REMOTE