Senior Machine Learning Engineer, Ads
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QuoraOnline advertising
This position can be performed remotely from multiple countries around the world. Listing locations: USA, Canada, Ireland. Please visit careers.quora.com/eligible-countries for details regarding employment eligibility by country., Availability for meetings and impromptu communication during Quora's coordination hours (Mon-Fri: 9am-3pm Pacific Time)Full-TimeSenior
SalaryUS based applicants: $189,507 - $274,604 USD + equity + benefits. Canada candidates: Toronto and Vancouver: $243,330 - $282,076 CAD; all other locations in Canada: $227,108 - $263,271 CAD.
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Job Details
- Experience
- 4+ years of professional software development experience in machine learning
- Required Skills
- PythonMachine LearningPyTorchTensorflowA/B testingDeep Learning
Requirements
- Bring 4+ years of professional software development experience in machine learning.
- Have hands-on experience developing and deploying ads ranking models at scale, including CTR or CVR prediction and calibration, with ownership of production improvements.
- Have experience evaluating ranking models through offline analysis and online experiments, including investigating discrepancies between model metrics and business outcomes.
- Have experience using AI-assisted development tools for coding, testing, debugging, or data analysis, and validate generated code and conclusions.
- Have hands-on experience building and deploying deep learning models with PyTorch or TensorFlow.
- Have a good understanding of the mathematical foundations of machine learning algorithms.
- Have strong Python programming skills and experience writing maintainable production ML code.
- Hold a BS, MS, or PhD in Computer Science, Engineering, or a related technical field.
- Be available for meetings and impromptu communication during Quora's coordination hours, Monday through Friday, 9am-3pm Pacific Time.
- Preferred: Experience with modern ranking architectures such as feature interaction networks, attention-based user-sequence models, and multi-task learning.
- Preferred: Understanding of how ranking predictions and calibration interact with bidding and auctions to affect ad delivery and advertiser outcomes.
- Preferred: Experience with large-scale multi-engineer projects, ranking challenges such as sparse or delayed conversion labels, sampling and exposure bias, cold-start users, or training-serving inconsistencies, or generative recommender systems.
Responsibilities
- 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 and 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 to other parts of the Ads product.
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