Senior Machine Learning Engineer

2 weeks ago


Singapore GRABTAXI HOLDINGS PTE. LTD. Full time
Roles & Responsibilities

Life at Grab
At Grab, every Grabber is guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles - the 4Hs: Heart, Hunger, Honour and Humility. These principles guide and help us make decisions as we work to create economic empowerment for the people of Southeast Asia.


Get to know the Team
GrabAds allows Advertisers, Agencies, and Merchants to engage the Grab Audience via innovative online and offline advertising experiences. Through our unique ecosystem and transacting user base, our clients are able to reach their desired audiences at scale, while driving measurable business outcomes. We would want to invite talented and passionate engineers to join our journey, to create value for our consumers, drivers, merchants, and advertising partners, while continuing to make everyday lives better across Southeast Asia.

Get to know the Role
You will develop machine learning models and strategies to predict the most relevant items in the user journey. You will drive real-world impact through innovative data-driven technology. You will drive the development and iteration of the algorithm and ensure that improvements roll out successfully.

The Day-to-Day Activities

  • Conceptualise and develop machine learning models to understand, classify and predict user behaviors in different scenarios. Deploy, test and maintain them.
  • Design and conduct both offline and online experiments to validate hypotheses.
  • Design and implement algorithms to derive deep insights and identify trends, patterns and relationships from high-volume high-dimensional data.
  • You are required to participate in code and design reviews with peers to maintain our high-standard engineering practice.
  • You will participate in on-call rotations with engineers. Troubleshoot, mitigate, and resolve issues.


The Must-Haves

  • You have Heart, Hunger, Honour and Humility.
  • Master's Degree or above in Computer Science, Electrical Engineering, Software Engineering, Automation, Data Science, or other Computer Science related majors.
  • You have a minimum of 2 years of relevant experience in developing Machine Learning models for systems including Ads/RecSys/Search.
  • Be familiar with model optimization methods and tricks, and be familiar with deep learning frameworks (TensorFlow/ps-lite/PyTorch)
  • Familiar with one or more of the following programming languages: Python/Golang/Java/C++
  • Be familiar with the Hadoop ecosystem, and be familiar with modern data processing stacks (e.g. Hive, Presto, Spark, Kafka, HBase, Flink etc.)
  • Strong understanding of statistics, data analysis, and data visualization techniques.
  • Excellent communication and collaboration skills with a team-oriented mindset, with the ability to work effectively with cross-functional teams.

The Nice-to-Haves

  • Be proactive and always be ready to tackle challenging tasks.
    Collaborating with multiple teams to facilitate rapid growth and development.
  • Be familiar with user behavior modeling, feature crossing technique, bid optimization strategy including oCPC, and keyword recommendation.
  • Proven track record/ accolades in data/ AI-related competitions (e.g. KDD Cup/Kaggle) or programming competitions (e.g. ICPC).
  • Proficient in one or more of the following programming languages: Golang/Java/C++/Scala
  • Experience in machine learning with cloud platforms (e.g. AWS)

Our Commitment
We are committed to building diverse teams and creating an inclusive workplace that enables all Grabbers to perform at their best, regardless of nationality, ethnicity, religion, age, gender identity or sexual orientation and other attributes that make each Grabber unique.


Tell employers what skills you have

Machine Learning
MongoDB
Data Analysis
Modeling
Architectural
Hadoop
Software Engineering
PyTorch
Teamoriented
Writing
Statistics
Data Science
Debugging
Humility
Electrical Engineering
Data Visualization
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