Staff Machine Learning Engineer

PayPal•San Jose, CA

3d•$193,978 - $333,500•Hybrid

About The Position

PayPal, Inc. seeks Staff Machine Learning Engineer in San Jose, CA. Develop and implement advanced ML models, such as gradient boosted decision tree, graph neural networks and deep learning models, to solve critical business problems related to recommendation of PayPal products, personalizing product experiences including UI flows, and optimizing the lifecycle of the customers on the platform. Design and deploy scalable generative AI solutions as part of the ecosystem. Design and deploy scalable ML/AI solutions that enhance PayPal's ability to provide a seamless customer experience, by working closely with our engineering group and PayPal's Platforms organization. Communicate complex concepts and the results of models and analyses to both technical and non-technical audiences, influencing partners and customers with insights and expertise. Partial telecommuting permitted from within a commutable distance.

Requirements

Master’s degree, or foreign equivalent, in Computer Science, Engineering, Physical Systems, or a closely related field plus four years of experience in the job offered or a related occupation. Employer will accept a Bachelor’s degree, or foreign equivalent, in Computer Science, Engineering, Physical Systems, or a closely related field plus six years of experience in the job offered or a related occupation.
Experience with machine learning (ML) models, Reinforcement learning with contextual multiarmed bandits and Neural Bandit. (3 years)
Experience with fine tuning LLMd such as Llama, RoBERTa with PyTorch or Tensorflow using algorithms including LoRA (1 year).
Experience with PyTorch, Tensorflow, Scala, Java, and Python (4 years).
Experience working on low latency system and writing skills with blogs or papers (2 years).
Experience with training, testing and productionizing ranking models for real-time recommendation systems with strict latency constraints. (3 years)
Experience with designing highly scalable near real-time feature engineering pipelines using Apache Flink for stream processing, Apache Kafka as queue and Apache Spark for batch feature processing. (3 years)
Experience with training, testing and productionizing Two-Tower based Neural Networks for generating personalized recommendations for users. (3 years)
Experience with performing statistical analysis for robust unbiased training data using techniques such as power analysis, Inverse Propensity Weighting and Design Effect. (4 years)

Responsibilities

Develop and implement advanced ML models, such as gradient boosted decision tree, graph neural networks and deep learning models, to solve critical business problems related to recommendation of PayPal products, personalizing product experiences including UI flows, and optimizing the lifecycle of the customers on the platform.
Design and deploy scalable generative AI solutions as part of the ecosystem.
Design and deploy scalable ML/AI solutions that enhance PayPal's ability to provide a seamless customer experience, by working closely with our engineering group and PayPal's Platforms organization.
Communicate complex concepts and the results of models and analyses to both technical and non-technical audiences, influencing partners and customers with insights and expertise.