Machine Learning Engineer
08 Oct 2019
Grab is more than just the leading ride-hailing and mobile payments platform in Southeast Asia. We use data and technology to improve everything from transportation to payments and financial services across a region of more than 620 million people. We work with governments, drivers, passengers, merchants, and the community, to solve critical problems in Southeast Asia.
Grab’s Data Science Department works on some of the most challenging and fascinating problems in transport, economics, logistics, and the space around. We apply machine learning, simulation, forecasting, scheduling, optimization, and many other advanced techniques on our huge datasets to push our business metrics to their bounds, directly and indirectly. We foster a culture where we enjoy raising the bar constantly for ourselves and others, and that strongly supports the freedom to explore and innovate.
Get to know the Role:
• Work on problems that run the gamut from building Machine Learning / Deep Learning models to developing full-scale production systems.
• Train and Predict on Grab’s unique large-scale data sets, leveraging Grab’s unique position as South-East Asia’s most popular Super App
• Build, deploy, maintain and optimize machine learning-based solutions, including computer vision and search-related applications
• Analyze data and define metrics for feature evaluation and model performance.
• Design and implement robust data pipelines.
• Identify and build new approaches and methods for machine learning as we grow.
• Bachelor/Master/PhD Degree in Computer Science, Math, EE or similar field.
• Minimum 2 years experience as a software engineer writing production code
• Solid software engineering and coding skills. In addition to Python, experience in at least one backend language like Go, Scala, Java, C++ or other is required
• Solid understanding of Machine Learning / Deep Learning and the existing frameworks such as Tensorflow and PyTorch
• Experience with cloud-based big data and machine learning services is a plus
• Self-motivated, curious, team-player, problem solver
• Detail-oriented and focused in a dynamic and fast-paced working environment.
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