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

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