Senior Machine Learning Engineer, Ads - Quora (Remote)

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

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

Perks and Benefits:

• Medical/dental/vision coverage
• Equity refreshers
• Remote work reimbursement
• Paid time off
• Employee assistance programs
• Competitive salary based on experience, location, education, and business needs