The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
This company is a leading destination for short-form mobile video. Their mission is to inspire creativity and bring joy. Global headquarters are in Los Angeles and Singapore, and offices include New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Job Description
Machine Learning Engineer
Why Join Us
Creation is the core of this company’s purpose. Their products are built to help imaginations thrive. This is doubly true of the teams that make their innovations possible. Together, they inspire creativity and enrich life – a mission they aim towards achieving every day. To them, every challenge, no matter how ambiguous, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always. At this company, they create together and grow together. That’s how they drive impact-for themselves, their company, and the users they serve. Join them.
The Business Risk Integrated Control (BRIC) team is missioned to:
Protect users, including and beyond content consumers, creators, advertisers;
Secure platform health and community experience authenticity;
Build infrastructures, platforms and technologies, as well as to collaborate with many cross-functional teams and stakeholders.
The BRIC team works to minimize the damage of inauthentic behaviors on their platforms, covering multiple classical and novel community and business risk areas such as account integrity, engagement authenticity, anti spam, API abuse, growth fraud, live streaming security and financial safety (ads or e-commerce), etc.
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. Their 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
Develop machine learning solutions to proactively address and mitigate business risks across the company’s products and platforms. These risks include, but are not limited to, abusive accounts, fake engagements, spam redirection, scraping, fraud, and others.
Enhance modeling infrastructure, labeling processes, feature engineering, and algorithms to improve robustness, automation, and generalization, while reducing both modeling and operational burden on risk management teams and easing the integration of new products and risk areas.
Drive improvements in risk-related machine learning practices, focusing on privacy and compliance, interpretability, and advanced risk perception and analysis.
Qualifications
Minimum Qualifications:
Degree or above in computer science, statistics, 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.
Preferred Qualification:
Ability to think critically, objectively, rationally. Reason and communicate in result-oriented, data-driven manner. High autonomy.
Disclaimer
SpringCube curates tech job listings from various company websites to support tech professionals in Singapore.
No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
No Client Relationship: This company is not a client of SpringCube unless stated.
To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.
No Liability: SpringCube is not liable for inaccuracies.
Save
