Machine Learning Engineer

UNITECH MECHATRONICS PTE. LTD.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading tech firm in Singapore seeks an experienced machine learning engineer to develop solutions for mitigating risks in eHealthcare products. Ideal candidates will have a Master's degree in a relevant field and solid engineering skills, especially in Linux and machine learning frameworks. Responsibilities include building robust machine learning solutions and improving operational infrastructures, ultimately contributing to enhanced privacy and compliance measures.

Qualifications

• Master or above degree in CS, EE or related fields.
• Solid engineering skills with proficiency in Linux, Hadoop, Hive, Spark or Storm.
• Strong machine learning background in modern theories and applications.

Responsibilities

• Build machine learning solutions for eHealthcare risks.
• Improve modeling infrastructures and reduce operational load.
• Enhance machine learning excellence in privacy and compliance.

Skills

Machine learning

Linux

Hadoop

Hive

Spark

Storm

Critical thinking

Education

Master or above degree in CS, EE or machine-learning-heavy majors

Job description

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:

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

Qualifications:

• Master or above degree in CS, EE or other relevant, machine-learning-heavy majors.
• Solid engineering skills. Proficiency in at least two of: Linux, Hadoop, Hive, Spark, Storm.
• 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.
• Ability to think critically, objectively, rationally. Reason and communicate in result-oriented, data-driven manner. High autonomy.