Intern, Machine Learning Engineer

Company Description

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.

Job Description

(Intake period) Candidates should be available for this internship in July 2024

This opportunity is only for students who must be able to commit for at least 3 months or up to 6 months for an internship. (Please specify your internship durations on your resumes - fail to do so may affect your candidature)
 

Get to know the team 

The AIP team is a platform team that builds the AI Platform that powers decision making and automation at Grab. We build tools for Machine Learning, Experimentation, Customer Behavioral Data and AI driven Automation. Everyone in this team is deeply passionate about the impact that AI can have on our business.
 

Get to know the role 

We are seeking passionate Interns, Machine Learning Engineer who have experience with machine learning and optimizations. You will have the opportunity to work on either core backend service engineering or full stack development or big data processing and training large scale ML models. It is very important that our team members take initiatives to identify problems, and have the right mindset and skill sets to solve them.
 

The Day-to-Day Activities

• Develop AutoML capabilities for adaptive experiments and general ML services
• Create library of generic surrogate model and acquisition function
• Construct evaluation and model selection loop
• Create generic tools for evaluation and visualizations
• Build feedback loops to improve systems

Qualifications

The Must-Haves

• Currently pursuing a relevant degree (Bachelor’s or Master’s) in Computer Science, Software Engineering, or a related field.
• Strong Computer Science fundamentals in algorithms and data structures.
• Proficiency in Python.
• Proficiency with any ML framework, such as TensorFlow or PyTorch.
• Familiarity working with VCS such as git, git-flow, understanding of full software development life-cycle.
• Experience with any big data framework, such as Spark, Ray, familiar with the concept of processing events in real-time
   

The Nice-to-Haves

• Experience building production machine learning systems
• Experience experimenting with foundation models (e.g., LLMs)

Additional Information

Our Commitment

We recognize that with these individual attributes come different workplace challenges, and we will work with Grabbers to address them in our journey towards creating inclusion at Grab for all Grabbers.