About this role.
Quora is seeking a senior machine learning engineer to specialize in advertising ranking for its Monetization team. The role owns the full production ML lifecycle, including data pipelines, feature engineering, model training, evaluation, deployment, and maintenance. Core work focuses on improving CTR and CVR prediction, calibration, user and ad representations, and sequence modeling while balancing latency, reliability, and cost. The engineer will partner with product, data science, ML platform, and engineering teams to run experiments and translate ranking improvements into advertiser value, revenue, and user relevance.
Role DNA
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Job Complexity
5/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
4/5Salary analysis
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Core skills
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Sample interview questions
I would explain the baseline, training data, features, objective function, and serving constraints, then quantify offline gains such as AUC or log loss. I would also describe the controlled online experiment, guardrail metrics, statistical approach, and the resulting impact on CTR, conversion quality, revenue, and user experience.
I would first validate logging, feature parity, and training-serving consistency. Then I would examine calibration, position and exposure bias, auction interactions, traffic segmentation, delayed labels, and whether the offline metric reflects the actual optimization objective; I would use targeted slices and follow-up experiments to isolate the cause.
I would consider delayed-feedback correction, censoring-aware labels, temporal weighting, proxy objectives, multi-task learning with denser engagement signals, and carefully designed negative sampling. I would validate each method by cohort and conversion-window maturity to ensure it improves long-term outcomes rather than only early label availability.
I would begin with a clear latency budget and select an architecture such as a compact attention-based sequence encoder or a precomputed embedding approach. I would use efficient feature retrieval, offline or nearline embedding computation where appropriate, model compression, and online monitoring for latency, feature freshness, and prediction quality.
Calibration makes predicted probabilities more meaningful for downstream ranking and bid calculations, helping the system compare candidates fairly across segments. I would assess calibration globally and by important slices, then evaluate how calibration changes alter delivery, spend, conversion efficiency, auction participation, and advertiser-level performance.
[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:
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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