Algo / Machine Learning Engineer, Senior Engineer (User Growth & Acquisition) - Paid Ads
Job Description
Job Description:
- Participate in the development of a large-scale Ads system
- Build industry-leading algorithms to improve ads pipelines for paidads, including recall/pre-rank/ranking etc.
- Involved in the design, development and maintenance of machine learning training data jobs for advertising.
- Design and implement, deliver machine learning models to production to ensure our algorithms give real impact to ads business.
- Research and develop Ads Bidding algorithms, ads traffic control, etc
- Responsible for the design and implementation of algorithms focused on user acquisition, activation, and retention for both new and existing customers.
- Collaborate with cross-functional teams, including software engineers, product managers, to integrate machine learning/strategy solutions into the production.
Requirements:
- Bachelor's degree or higher in Computer Science, Data Science, Machine Learning, or a related field.
- 2+ years of experience with search/recommendation/ads systems and various machine learning algorithms, especially for matching/ranking models, such as DSSM, DIN, MMoE.
- Experience with machine learning frameworks and libraries, such as TensorFlow, PyTorch, etc. And experience with LLM libraries, such as hugging face, vLLM, etc.
- Knowledge of data analysis,feature engineering and model training/evaluation/distill/finetune/deployment.
- Skilled in applying causal inference techniques and building uplift models to optimize marketing strategies and user engagement.
- Excellent problem-solving and analytical thinking abilities, with a focus on delivering practical and scalable solution
- Familiar with Python, Spark, Hive, Tensorflow, etc.
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Key Skills
About Company
Skills:
Tensorflow, Machine Learning, Pytorch, Hive, Python, Spark, Data Analysis, vLLM, DIN, feature engineering, distill, uplift models, DSSM, hugging face, causal inference techniques, search recommendation, MMoE, ads systems, model training, Deployment, finetune, Evaluation