Lead 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

Get to know our Team

Grab is Southeast Asia’s leading super-app. We provide everyday services such as deliveries, mobility, financial services, enterprise services and others to millions of users across the region. At the AI Automation Team, we build end-to-end automated ML/AI solutions to solve challenging problems in Grab’s marketplace. The problem space we are working on consists of adaptive experiments, embeddings, recommendations and large scale marketplace optimizations. We are looking for lead machine learning engineers to join the team to help us make that vision a reality by developing and refining cutting-edge ML and experiment platforms.

Get to know the Role

This is a hands-on role involving building large-scale machine learning, reinforcement learning and optimization solutions. You will have the opportunity to build innovative data-driven technologies to drive real-world impact in Grab’s marketplace that consists of tens of thousands of consumers, drivers and merchants.

The ideal candidate will have strong background in machine learning, reinforcement learning and optimization, good experience developing in scalable machine learning solutions to solve complex problems, solid understanding of software development life-cycle and engineering practices, experience in working on a range of regression/classification and optimization problems.

The day-to-day activities

• Lead the design, development, and launch of innovative machine learning models.
• Work closely with cross-functional teams to understand their needs and optimize solutions.
• Architect and develop reinforcement learning frameworks to train and run reinforcement learning algorithms at scale.
• Manage machine learning applications through their entire lifecycle.
• Design and implement algorithms to derive deep insights and identify trends, patterns and relationships from high-volume high-dimensional data.
• Participate in code and design reviews with peers to maintain our high-standard engineering practice.
• Participate in on-call rotations with engineers. Troubleshoot, mitigate, and resolve issues.
• Mentor junior machine learning engineers and provide insightful feedback.
  
  

Qualifications

The must-haves

• A degree in computer science, software engineering, information technology or related fields
• 5+ years of experience in one or more of the following areas: general machine learning, deep learning, reinforcement learning
• Solid understanding of engineering practices and design patterns, experience in writing readable, maintainable and testable code
• Solid understanding of turning business problems into ML/AI-projects
• Proficiency in working with VCS such as git, git-flow, understanding of full software development life-cycle
• Proficiency in any ML framework, such as TensorFlow or PyTorch
• Proficiency in Python
• Proficiency in any big data framework, such as Spark and Ray, familiar with the concept of processing events in real-time
• Experience developing production quality ML Pipelines
  
  

The nice-to-haves

• A Masters or PhD in computer science, machine learning or related fields
• Experience with MLFlow, TensorFlow probability, TensorFlow agents, Ray RLLib
• Experience in Golang/Rust
• Experience with distributed systems and cloud services (AWS, GCP, AZURE)
• Experience applying reinforcement learning for solving real-world problems (robotics, finances, etc.).
• Understanding of probabilistic modeling and differential programming, ability to design/build probabilistic simulators
• Contributions to open source projects
  
  

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.