Senior Machine Learning Engineer, Ads

• Develop and improve ads ranking models, including prediction objectives, feature interactions, user-history modeling, and calibration
• Own machine learning systems end-to-end, including data pipelines, feature engineering, training-data construction, model evaluation, model training, and production integration
• Evaluate and apply advances in deep learning and recommendation modeling within production latency, reliability, and cost constraints
• Collaborate with ML platform and product engineers to build scalable and efficient production machine learning systems
• Partner with product, data science, and engineering teams to define ranking objectives, design A/B experiments, and measure advertiser performance, revenue, and user relevance
• Identify opportunities to apply machine learning across the Ads product
• Improve CTR and CVR prediction, model calibration, user and ad representations, and user-sequence modeling
• Translate ranking-quality improvements into advertiser value, revenue, and better user experiences

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
• Demonstrated ownership of production improvements
• Experience evaluating ranking models through offline analysis and online experiments
• Experience investigating discrepancies between model metrics and business outcomes
• Experience using AI-assisted development tools for coding, testing, debugging, or data analysis
• 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
• Experience writing maintainable production ML code
• BS, MS or PhD in Computer Science, Engineering or a related technical field
• Preferred: experience with modern ranking architectures, feature interaction networks, attention‑based user‑sequence models, and multi‑task learning
• Preferred: understanding of ranking predictions and calibration with bidding and auctions
• Preferred: experience with large‑scale multi‑engineer projects
• Preferred: experience addressing sparse or delayed conversion labels, sampling and exposure bias, cold‑start users, and training‑serving inconsistencies
• Preferred: experience with generative recommender systems

Core Competencies

Demonstrates expertise in developing and deploying machine learning models, particularly in ads ranking, with a strong focus on improving CTR and CVR predictions. Proficient in collaborating with cross‑functional teams to enhance model performance and translate technical improvements into business value.

Highest-signal resume keywords

• Machine Learning Systems Ownership
• Ads Ranking Model Development
• Deep Learning with PyTorch or TensorFlow
• Python Programming
• Model Evaluation and A/B Testing

ATS Optimization Keywords

Hard Skills

• Machine Learning
• Ads Ranking Models
• CTR Prediction
• CVR Prediction
• Model Calibration
• Feature Engineering
• Deep Learning
• Data Pipelines
• Model Training
• Production Integration

Soft Skills

• Sound Judgment
• Collaboration
• Communication

Certifications & Qualifications

• BS in Computer Science
• MS in Engineering
• PhD in Related Technical Field

Industry Keywords

• User-History Modeling
• Feature Interaction Networks
• Attention-Based User-Sequence Models
• Multi-Task Learning
• Generative Recommender Systems

Tools & Technologies

• PyTorch
• TensorFlow
• AI-Assisted Development Tools