Get to know the Team
Grab’s Data Science Teams work on some of the most challenging and fascinating problems in transport, economics, logistics, and the space around. Our data scientists apply a myriad of advanced techniques to solve complicated and challenging business problems. The ML Platform team seeks to develop scalable and robust machine learning infrastructure and tooling at Grab, empowering our users to deploy ML models at scale in production from end to end in a safe and continuous manner.
Get to know the Role
- Be involved in the end to end lifecycle of Machine Learning, understanding the Data Science journey and building the right tools for our users
- Develop tools and services that enable ML practitioners to build robust machine learning pipelines that adhere to the principles of continuous delivery
The Day-To-Day Activities
- Build and maintain scalable and flexible model training frameworks using containerisation technologies such as Kubernetes, allowing users to run heterogeneous workloads using the right open source technologies
- Build and maintain a cost efficient model serving platform at scale, flexible and robust enough to scale up and down depending on traffic patterns
- Collaborate with data scientists and ML engineers from different functions to empower them to deploy their machine learning solutions to production to solve business problems
The Must-Haves
- 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
- Decent understanding of Machine Learning / Deep Learning and the existing frameworks such as Tensorflow and PyTorch
- Strong understanding of distributed ETL frameworks like Spark or Scalding
- 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.