In this team you'll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in building scalable and robust, intelligent and privacy-safe, secure and product-friendly systems and solutions. Our challenges are not some regular day-to-day technical puzzles -- You'll be part of a team that's developing novel solutions to first-seen challenges of a non-stop evolvement of a phenomenal product eco-system. The work needs to be fast, transferrable, while still down to the ground to making quick and solid differences.

Responsibilities:

l Build machine learning solutions to respond to and mitigate business risks in eHealthcare products/platforms. Such risks include and are not limited to abusive accounts, fake engagements, spammy redirection, scraping, fraud, etc.

l Improve modeling infrastructures, labels, features and algorithms towards robustness, automation and generalization, reduce modeling and operational load on risk adversaries and new product/risk ramping-ups.

l Uplevel risk machine learning excellence on privacy/compliance, interpretability, risk perception and analysis.

Qualifications:

l Master or above degree in CS, EE or other relevant, machine-learning-heavy majors.

l Solid engineering skills. Proficiency in at least two of: Linux, Hadoop, Hive, Spark, Storm.

l Strong machine learning background. Proficiency or publications in modern machine learning theories and applications such as deep neural nets, transfer/multi-task learning, reinforcement learning, time series or graph unsupervised learning.

l Ability to think critically, objectively, rationally. Reason and communicate in result-oriented, data-driven manner. High autonomy.