Staff Machine Learning Engineer, Recommendation Systems - Quora (Remote)
ML Engineer · Staff · Full Time · Remote
Opens Quora's application page
Role
What you'll do.
Staff Machine Learning Engineer leading Quora's recommendation systems and feed ranking architecture. This role requires 6+ years of ML experience with 4+ years specializing in home feed recommendations for social platforms. You'll own the full recommender stack, drive generative AI integration, mentor engineers, and shape technical direction for one of the world's largest knowledge-sharing platforms with millions of daily users.
Responsibilities
- Technical Direction of Recommendation Ecosystem: Define and set the technical strategy for Quora's home feed recommendation systems, including candidate generation, retrieval, ranking, re-ranking, and multi-objective optimization across the full recommender stack.
- Lead High-Impact ML Projects: Own end-to-end machine learning systems from conception through production deployment, including data pipeline architecture, feature engineering, model training, experimentation, and production integration for measurable user engagement improvements.
- Drive Generative AI Integration: Pioneer the deployment and scaling of LLMs and generative models within the recommendation ecosystem to enhance content understanding, ranking mechanisms, and personalization at scale.
- Design and Execute Online Experiments: Drive measurable improvements in user engagement and content quality through rigorous A/B testing, statistical analysis, and data-driven decision making to validate model changes and ranking algorithms.
- Scale Recommendation Surfaces: Extend recommendation systems beyond the home feed to notifications, digest emails, and other product surfaces while maintaining architectural consistency and performance standards.
- Architecture ML Abstractions and Tools: Build reusable tools, frameworks, and abstractions that empower the broader engineering organization to accelerate ML development velocity and enable AI-assisted development practices.
- Mentor and Lead Engineers: Guide rising technical talent, establish best practices for code quality and modeling rigor, and actively mentor engineers on machine learning systems design and production-grade implementations.
- Cross-Functional Partnership: Collaborate with product, data science, and infrastructure teams to align on priorities, evaluate technical tradeoffs, and identify new machine learning opportunities across the Quora platform.
Qualifications
What we look for.
Technical
Recommender Systems Expertise
Deep hands-on experience building production recommender systems with mastery of candidate generation, retrieval algorithms, ranking models, re-ranking strategies, embedding techniques, and multi-objective optimization frameworks.
Machine Learning Foundations
Strong mathematical understanding of ML algorithms, statistical methods, optimization theory, and ability to reason about algorithmic tradeoffs between computational efficiency, model performance, and system latency.
Production ML Systems
Proven ability to architect, deploy, and operate machine learning systems at scale including data pipelines, feature stores, model serving infrastructure, and monitoring frameworks in production environments.
Python or C++ Proficiency
6+ years of professional experience writing production-grade code in Python, C++, or both, with ability to write efficient, maintainable, and scalable systems code.
Generative AI and LLM Experience
Hands-on experience deploying, fine-tuning, and scaling large language models and generative AI systems in production, including experience with model optimization and inference acceleration.
Experimentation and Analytics
Expertise in designing and analyzing online experiments, A/B testing frameworks, statistical significance testing, and metrics design for recommendation systems.
Education
Computer Science Degree
Bachelor's, Master's, or PhD degree in Computer Science or equivalent field providing strong foundational knowledge in algorithms, data structures, and computational theory.
Experience
6+ Years ML Development
Six or more years of professional software development experience specifically in machine learning, including model development, system design, and production deployment.
4+ Years Home Feed Recommendations
Four or more years of direct experience building, optimizing, and shipping home feed recommendation systems for social media or user-generated content platforms at scale.
End-to-End Project Leadership
Demonstrated experience leading large-scale machine learning projects from ideation through production launch with measurable business outcomes and impact metrics.
Notifications and Lifecycle Modeling
Background in building recommendation systems for notifications, email digests, personalized outreach, or growth and lifecycle modeling applications.
Skills
Required
Recommendation Systems Architecture
Expert-level understanding and implementation experience with the complete recommender pipeline including candidate generation, retrieval, ranking, re-ranking, and serving optimizations.
Machine Learning Engineering
Production ML systems design, model development, training pipeline optimization, feature engineering, and ability to ship ML models to millions of users.
Python Programming
Expert proficiency in Python for data processing, model development, experimentation frameworks, and production systems engineering.
Statistical Analysis and Experimentation
Strong statistical foundations with expertise in experimental design, A/B testing, causal inference, and metrics analysis for recommendation systems.
System Design and Scalability
Ability to design systems that process billions of events, optimize latency and throughput, and scale recommendation serving to millions of concurrent users.
Leadership and Communication
Demonstrated ability to define technical strategy, communicate complex ML concepts across technical and non-technical audiences, and guide engineering teams through architectural decisions.
Preferred
Large Language Models and Generative AI
Nice to haveHands-on production experience with LLM deployment, fine-tuning, prompt engineering, and integration of generative models into recommendation systems.
C++ Systems Programming
Nice to haveProficiency in C++ for high-performance systems code, real-time serving infrastructure, or performance-critical components in ML systems.
Research Publications
Nice to haveTrack record of publishing research at top-tier conferences such as RecSys, KDD, NeurIPS, ICML, or SIGIR demonstrating thought leadership in recommendation systems or machine learning.
Distributed Systems Engineering
Nice to haveExperience designing and operating distributed ML systems, real-time serving platforms, and large-scale data processing infrastructure.
Embedding and Vector Retrieval
Nice to haveExpertise with modern embedding techniques, approximate nearest neighbor search, vector databases, and efficient retrieval methods for production recommendation systems.
Multi-Objective Optimization
Nice to haveExperience balancing competing objectives in recommendation systems such as engagement, content quality, diversity, and user satisfaction using multi-objective optimization techniques.
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 219,989 – 320,613
Equity·Stock options
Benefits
Medical, Dental, and Vision Coverage
Comprehensive health insurance covering medical, dental, and vision care with country-specific plan options and subsidies.
Equity Refreshers
Annual equity grants and refresh cycles enabling ongoing ownership and alignment with company success beyond initial grant.
Flexible Equity Program
In equity-eligible countries, option to take a portion of equity compensation as cash for personalized financial planning flexibility.
Remote Work Reimbursement
Monthly stipend for remote workspace setup, equipment, internet, and other remote work infrastructure supporting distributed work environment.
Paid Time Off
Generous paid vacation, sick leave, and personal days with flexibility to recharge and maintain work-life balance.
Employee Assistance Programs
Comprehensive support services including mental health resources, counseling, financial planning, and wellness programs.
Country-Specific Benefits
Additional benefits tailored to local regulations and requirements in your employment country of residence.
Process
Interview steps.
- 01
Application and Resume Review
Submit application through Ashby platform with resume and cover letter. AI technology assists in initial screening while all decisions are made by hiring team members. Focus on demonstrating recommendation systems expertise and measurable impact from past ML projects.
- 02
Recruiter Phone Screen
Initial conversation with recruiting team covering background, motivation for the role, remote work experience, and alignment with Quora's coordination hours (Mon-Fri 9am-3pm PT). Discuss career trajectory in machine learning and recommendation systems.
- 03
Technical System Design Interview
Deep dive into recommendation system architecture design, focusing on how you would build components like candidate generation, ranking models, and retrieval systems at scale. Be prepared to discuss tradeoffs between latency, accuracy, and computational cost.
- 04
Machine Learning and Modeling Interview
Technical assessment of ML fundamentals, algorithm knowledge, and ability to formulate recommendation problems mathematically. Discuss past projects, how you approached optimization, and your experience with embeddings, ranking losses, or multi-objective optimization.
- 05
Leadership and Mentoring Discussion
Conversation with hiring manager about leadership philosophy, mentoring experience, cross-functional collaboration, and how you've influenced technical direction. Discuss examples of raising bar on engineering practices and driving organizational impact.
- 06
Generative AI and Modern ML Trends
Discussion of experience with LLMs, generative models, and emerging AI techniques. Share examples of how you've integrated newer technologies into production systems and your vision for applying generative AI to recommendations.
- 07
Compensation and Logistics Discussion
Finalists discuss compensation package including base salary within range of $219,989-$320,613 USD, equity refreshers, and benefits. Clarify employment eligibility for remote work and coordination hours availability across your timezone.
- 08
Background Check and Identity Verification
Final candidates undergo comprehensive background check and identity verification prior to official offer and onboarding process.
Full posting
Original listing.
[Quora is a privately held, "remote-first" company. This position can be performed remotely from multiple countries around the world. Please visit careers.quora.com/eligible-countries for details regarding employment eligibility by country.]
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:
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.
This is a high-leverage, hands-on IC role with organization-wide scope. You will write production code, mentor rising talent, and actively shape how our team leverages AI-assisted development to accelerate our shipping velocity.
Responsibilities:
Set the technical direction for Quora’s home feed recommendation systems
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
Minimum Requirements:
Availability for meetings and impromptu communication during Quora's “coordination hours" (Mon-Fri: 9am-3pm Pacific Time)
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
Strong communication and interpersonal skills
BS, MS, or PhD in Computer Science
Preferred Requirements:
6+ years of experience writing Python or C++ code
Hands-on experience deploying and running generative models at scale
Background in notifications, email/digest recommendations, or growth and lifecycle modeling
Flexible and positive team player
Publications at venues like RecSys, KDD, NeurIPS, or ICML.
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, as we know that a diverse range of perspectives can have a significant impact on our products and our culture.
Additional Information:
We are accepting applications on an ongoing basis. This role is a backfill for an existing vacancy.
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. Benefits are country-specific and may vary.
There are many factors that will determine the starting compensation, including but not limited to experience, location, education, and business needs.
US candidates only: For US based applicants, the salary range is $219,989 - $320,613 USD + equity + benefits.
Canada candidates only: 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.
To ensure a secure hiring process, all final candidates will undergo identity verification and a comprehensive background check prior to onboarding.
Job Applicant Privacy Notice: https://careers.quora.com/quora-global-job-applicant-privacy-notice
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