Qishicpc is hiring: Machine Learning Engineer Intern in Palo Alto
Qishicpc, Palo Alto, CA, United States
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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
Benefits
OPPO 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 regarding 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.
Requirements
Minimum 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
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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
Benefits
OPPO 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 regarding 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.
Requirements
Minimum 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
#J-18808-Ljbffr