FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Senior Machine Learning Engineer, Ads
QuoraSenior Machine Learning Engineer improving Quora's ad ranking, prediction, and calibration systems. Owning ML research, deployment, experimentation, and production performance at scale.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
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 OwnershipAds Ranking Model DevelopmentDeep Learning with PyTorch or TensorFlowPython ProgrammingModel Evaluation and A/B Testing
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningAds Ranking ModelsCTR PredictionCVR PredictionModel CalibrationFeature EngineeringDeep LearningData PipelinesModel TrainingProduction Integration
Soft Skills
Sound JudgmentCollaborationCommunication
Tools & Technologies
PyTorchTensorFlowAI-Assisted Development Tools
Certifications & Qualifications
BS in Computer ScienceMS in EngineeringPhD in Related Technical Field
Industry Keywords
User-History ModelingFeature Interaction NetworksAttention-Based User-Sequence ModelsMulti-Task LearningGenerative Recommender Systems
Tech Stack
Tools & technologiesPythonPyTorchTensorflow
About the role
Key responsibilities & impact- 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
What you’ll need- 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
Benefits
Comp & perks- Medical coverage
- Dental coverage
- Vision coverage
- Equity refreshers
- Remote work reimbursement
- Paid time off
- Employee assistance programs
- Equity
- Benefits vary by country