Senior Machine Learning Engineer, Ads
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QuoraAdvertising Technology
This position can be performed remotely from multiple countries around the world. Please visit careers.quora.com/eligible-countries for details regarding employment eligibility by country., Mon-Fri: 9am-3pm Pacific TimeFull-TimeSenior
SalaryUS candidates only: $189,507 - $274,604 USD. Canada candidates: $243,330 - $282,076 CAD (Toronto/Vancouver) or $227,108 - $263,271 CAD (other). All include equity + benefits.
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Job Details
- Experience
- 4+ years
- Required Skills
- PythonMachine LearningPyTorchTensorflowA/B testingDeep Learning
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 using AI-assisted development tools for coding, testing, debugging, or data analysis
- Hands-on experience building and deploying deep learning models with PyTorch or TensorFlow
- Strong Python programming skills and experience writing maintainable production ML code
- Good understanding of mathematical foundations of machine learning algorithms
- BS, MS or PhD in Computer Science, Engineering or a related technical field
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.
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