Senior Machine Learning Engineer, Ads - Quora (Remote)
ML Engineer · Senior · Full Time · Remote
Opens Quora's application page
Role
What you'll do.
Senior Machine Learning Engineer specializing in ads ranking at Quora, responsible for developing and optimizing CTR/CVR prediction models, feature engineering, and end-to-end machine learning systems that directly impact advertiser performance and user experience across 300+ million monthly visitors. This remote-first role requires 4+ years of ML production experience with demonstrated expertise in scaling ranking models, deep learning frameworks, and A/B experimentation in high-stakes ad tech environments.
Responsibilities
- Ads Ranking Model Development: Design and develop machine learning models for ads ranking, focusing on improving CTR and CVR prediction accuracy, implementing feature interaction networks, user-history modeling, and calibration techniques to enhance ranking quality and revenue optimization.
- End-to-End ML System Ownership: Take full ownership of machine learning systems across the entire lifecycle—from constructing data pipelines and feature engineering to building training datasets, model evaluation, training, and seamless integration into production environments with continuous deployment.
- Deep Learning Architecture Evaluation: Evaluate and apply cutting-edge deep learning and recommendation system advances to improve ads ranking while maintaining strict production constraints around latency, reliability, and computational cost. Stay current with modern ranking architectures including attention-based sequence models and multi-task learning.
- Cross-Functional Collaboration: Partner with ML platform engineers, product teams, data scientists, and backend engineers to design scalable production systems, define ranking objectives, implement A/B testing frameworks, and translate model improvements into measurable advertiser ROI and user relevance gains.
- Experimentation and Measurement: Design and execute A/B experiments to measure improvements in advertiser performance, platform revenue, and user satisfaction. Investigate discrepancies between offline model metrics and online business outcomes to drive continuous optimization.
- ML Opportunity Identification: Proactively identify and propose new applications of machine learning across different components of the Quora ads platform, from targeting to auction dynamics to quality measurement, driving innovation and competitive differentiation.
Qualifications
What we look for.
Technical
Deep Learning Frameworks
Hands-on production experience building and deploying deep learning models using PyTorch or TensorFlow, with proficiency in model optimization, distributed training, and model serving at scale.
Python Programming Mastery
Advanced Python programming skills with demonstrated ability to write maintainable, efficient production-grade ML code. Experience with software engineering best practices including testing, debugging, version control, and code documentation.
ML Mathematical Foundations
Strong understanding of mathematical foundations underlying machine learning algorithms including optimization theory, statistical inference, probabilistic models, and gradient-based learning methods.
AI-Assisted Development Tools
Experience leveraging modern AI-assisted coding tools such as GitHub Copilot or Claude for code generation, testing, debugging, and data analysis, with demonstrated judgment in validating generated outputs against correctness and best practices.
Ads Ranking Models at Scale
Production experience developing and deploying large-scale ads ranking systems, including hands-on work with CTR prediction, CVR prediction, calibration techniques, and proven impact on business metrics through model improvements.
Model Evaluation and Experimentation
Expertise in evaluating ranking models through offline analysis techniques and online A/B experimentation. Ability to investigate and reconcile discrepancies between model metrics and real-world business outcomes.
Education
Computer Science or Engineering Degree
Bachelor's, Master's, or PhD degree in Computer Science, Engineering, or a closely related technical field with strong foundation in algorithms, mathematics, and software systems.
Experience
Professional ML Development
Minimum 4+ years of professional software development experience specifically in machine learning, with track record of delivering production ML systems that have driven measurable business impact.
Large-Scale ML Project Leadership
Preferred experience leading and executing large-scale, multi-engineer machine learning projects involving complex coordination across teams, managing dependencies, and delivering results on ambitious timelines.
Ads Tech and Auction Dynamics
Preferred understanding of how ranking predictions and model calibration interact with bidding mechanisms and auction dynamics to influence ad delivery, advertiser outcomes, and platform monetization.
Advanced Ranking Challenges
Preferred experience solving sophisticated ranking problems including sparse or delayed conversion labels, sampling bias, exposure bias, cold-start user scenarios, and training-serving inconsistencies in production systems.
Skills
Required
PyTorch or TensorFlow
Production-grade expertise with deep learning frameworks, including model architecture design, training optimization, and deployment strategies
Python
Advanced proficiency in Python with ability to write clean, tested, and maintainable production code
Machine Learning Theory
Deep understanding of ML algorithms, statistical methods, and optimization techniques applicable to large-scale systems
CTR/CVR Prediction
Hands-on experience building click-through rate and conversion rate prediction models in production environments
Feature Engineering
Expertise in designing, implementing, and optimizing feature pipelines for machine learning models at scale
A/B Testing and Experimentation
Proficiency in designing experiments, analyzing results, and drawing valid conclusions in online testing environments
Model Calibration
Technical knowledge of model calibration techniques and their importance in ads ranking and business metrics
Preferred
Attention Mechanisms and Transformers
Nice to haveExperience with attention-based architectures for sequence modeling and user behavior prediction in ranking systems
Multi-Task Learning
Nice to haveFamiliarity with multi-task learning frameworks that optimize multiple objectives simultaneously in ranking systems
Feature Interaction Networks
Nice to haveKnowledge of modern architectures that model feature interactions such as DeepFM, DCN, or other interaction-based models
Generative AI and Recommenders
Nice to haveExperience with generative recommendation systems and modern language models applied to content ranking or personalization
Recommender Systems
Nice to haveBackground in building large-scale recommendation systems applicable to ads delivery and ranking
Data Pipeline Architecture
Nice to haveExperience designing scalable data processing pipelines for machine learning, including ETL, feature stores, and real-time systems
ML Systems Design
Nice to haveUnderstanding of production ML systems architecture including feature serving, model serving, monitoring, and debugging in production
Leadership and Communication
Nice to haveStrong interpersonal and communication skills with ability to influence cross-functional stakeholders and articulate technical concepts to non-technical audiences
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 189,507 – 274,604
Equity·Stock options
Benefits
Medical, Dental, and Vision Coverage
Comprehensive healthcare benefits including preventive care, medical procedures, dental coverage, and vision correction
Equity and Refreshers
Ownership in Quora through equity grants with periodic refresher grants designed to maintain long-term value alignment
Remote Work Reimbursement
Financial support for home office equipment, internet connectivity, and other remote work infrastructure to ensure productive work environment
Paid Time Off
Generous paid vacation and personal time to support work-life balance and employee wellbeing
Employee Assistance Programs
Confidential counseling, financial planning, and wellness resources to support employee mental health and personal development
Flexible Remote-First Culture
Work from anywhere globally with high autonomy and flexibility, requiring coordination hours only during Mon-Fri 9am-3pm Pacific Time
Country-Specific Benefits
Additional benefits tailored to specific countries where employees are based, ensuring local relevance and competitiveness
Process
Interview steps.
- 01
Application Review
Initial screening of application materials with AI assistance for sorting, followed by human review focusing on relevant ML experience and technical background alignment with ads ranking expertise
- 02
Technical Screening Interview
Conversation with an ML engineer from the team assessing hands-on experience with deep learning frameworks, Python proficiency, understanding of ranking systems, and approach to production ML challenges
- 03
Ads Ranking Problem Discussion
Focused discussion on specific ads ranking challenges including CTR/CVR prediction, calibration approaches, model evaluation strategies, and how to bridge discrepancies between offline metrics and online performance
- 04
System Design Interview
Architectural discussion on designing large-scale ML systems—covering data pipelines, feature engineering at scale, model serving, monitoring, and handling production constraints around latency and cost
- 05
Cross-Functional Collaboration Assessment
Conversation with product managers, data scientists, or other stakeholders evaluating communication style, ability to work across teams, and understanding of how ML ranking impacts advertiser outcomes and user experience
- 06
Background Verification and Identity Check
Final candidates undergo comprehensive background checks and identity verification to ensure hiring integrity and security compliance prior to onboarding
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 platform providing millions of global users with one place to chat, explore and build with a wide variety of AI language models (bots), including GPT-5.6-Sol, Claude-Opus-5, Claude-Fable-5, Grok-4.6, Kimi-K3, and thousands of others. As AI capabilities rapidly advance, Poe provides a single platform to instantly integrate and utilize these new models.
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 Monetization team works on challenging problems every day. Our Machine Learning Engineers are tasked with optimizing the advertising product at Quora, and the team covers the entire Machine Learning Ads lifecycle from end-to-end, including ads targeting, ranking and auction dynamics, and quality measurement. Ingrained in our culture is the desire to constantly learn and improve, 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. As a remote-first company, our engineers have a high degree of flexibility and autonomy, and everyone on the engineering team has a huge impact on our product, revenue and company.
Since we first launched our advertising platform, we've grown to support thousands of advertisers who are reaching over 300 million+ monthly unique visitors on Quora. Our journey is just beginning as we continue to build new products from the ground up and tackle exciting challenges at scale. We are looking for an experienced Machine Learning Engineer to join the Ads ML team as an ads ranking specialist. You will improve CTR and CVR prediction, model calibration, user and ad representations, and user-sequence modeling, translating improvements in ranking quality into measurable advertiser value, revenue, and better user experiences. This is a small, close-knit team where you own problems end-to-end — research, data, modeling, deployment and maintenance — and where your work has a direct line to the company's top line.
Responsibilities:
Develop and improve ads ranking models, including prediction objectives, feature interactions, user-history modeling, and calibration
Take end to end ownership of machine learning systems - from data pipelines, feature engineering, training-data construction and model evaluation, model training, as well as integration into our production systems
Evaluate and apply advances in deep learning and recommendation modeling to improve ads ranking within production latency, reliability, and cost constraints
Collaborate with ML platform and product engineers to build scalable and efficient machine learning systems in the production environment
Partner with product, data science, and engineering teams to define ranking objectives, design A/B experiments, and measure improvements in advertiser performance, revenue, and user relevance
Identify new opportunities to apply machine learning to different parts of the Ads product to drive value for our users and advertisers
Minimum Requirements:
Availability for meetings and impromptu communication during Quora's “coordination hours" (Mon-Fri: 9am-3pm Pacific Time)
4+ years of professional software development experience in machine learning
Hands-on experience developing and deploying ads ranking models at scale, including CTR or CVR prediction and calibration, with demonstrated ownership of production improvements
Experience evaluating ranking models through offline analysis and online experiments, including investigating discrepancies between model metrics and business outcomes
Experience using AI-assisted development tools for coding, testing, debugging, or data analysis, with sound judgment in validating generated code and conclusions
Hands-on experience building and deploying deep learning models with PyTorch or TensorFlow
Good understanding of mathematical foundations of machine learning algorithms
Strong Python programming skills and experience writing maintainable production ML code. proficient coding ability writing Python
BS, MS or PhD in Computer Science, Engineering or a related technical field
Preferred Requirements:
Experience with modern ranking architectures, such as feature interaction networks, attention-based user-sequence models, and multi-task learning
Understanding of how ranking predictions and calibration interact with bidding and auctions to affect ad delivery and advertiser outcomes
Experience with leading large-scale multi-engineer projects
Experience addressing ranking challenges such as sparse or delayed conversion labels, sampling and exposure bias, cold-start users, or training-serving inconsistencies
Experience with generative recommender systems
Effective communicator with strong leadership skills
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 pay, including but not limited to experience, location, education, and business needs.
US candidates only: For US based applicants, the salary range is $189,507 - $274,604 USD + equity + benefits.
Canada candidates only: For Toronto and Vancouver based applicants, the salary range is $243,330 - $282,076 CAD + equity + benefits. For all other locations in Canada, the salary range is $227,108 - $263,271 CAD + equity + benefits.
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://www.careers.quora.com/pages/quora-global-job-applicant-privacy-notice
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