Senior Machine Learning Engineer, Ads - Quora (Remote)

[ 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