Senior Machine Learning Engineer, Ads

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.

- 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