About the Role:Join OPPO's core team to develop industrial-scale machine learning systems impacting millions of users. You'll tackle trillion-scale data challenges while balancing user experience and business monetization goals. Key Responsibilities: Business Impact through Algorithms Optimize recommendation/advertising systems using ML techniques to simultaneously enhance user engagement and revenue growth Conduct rigorous A/B testing and causal inference analysis to validate improvements Large-Scale Feature Engineering Design and implement feature pipelines processing trillion-scale user behavior data Develop innovative feature representation methods for industrial recommender systems Deep Learning System Optimization Architect neural network models balancing computational efficiency and predictive performance Innovate in ranking algorithms through model architecture improvements and multi-objective optimization RequirementsMinimum Qualifications: Pursuing BS/MS in Computer Science, AI, Mathematics, or related technical field Strong understanding of ML fundamentals: bias-variance tradeoff, regularization, ensemble methods Proficiency with Python and ML frameworks (PyTorch/TensorFlow) Experience building data pipelines with SQL/Pandas/Spark Preferred Qualifications: 4+ month full-time availability (priority given to candidates available for summer + fall) Hands-on experience with recommender systems through: Industry projects (ranking algorithms, CTR prediction) ML competitions (Kaggle Grandmaster preferred) Research publications (RecSys, KDD, WWW conferences) Familiarity with big data tools: Hadoop, Spark, Yarn BenefitsOPPO is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.The US base salary range for this full-time position is $30-$60/hour. Our salary ranges are determined by role, level, and location.
