Machine Learning Engineer, Foundation Models

About Grab and Our Workplace

Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.

Get to Know the Team

At the AI Automation Team, we strive to stay ahead by building comprehensive automated ML/AI solutions to solve complex challenges in Grab's marketplace. Our team focuses on areas like experiments, embeddings, recommendations, and large-scale marketplace optimizations. We're excited to welcome machine learning engineers who are passionate about advancing our mission by designing and refining innovative ML and experiment platforms.

Get to Know the Role

This is an applied research role aimed at developing foundation model solutions for Grab. Responsibilities include proposing and implementing efficient foundation model architectures as well as scalable data and model pipelines. The ideal candidate should have a strong background in modern machine learning and foundation model techniques, including pretraining, multimodality and finetuning. They should also have substantial experience in developing scalable machine learning solutions for complex problems and a solid understanding of the software development lifecycle and engineering practices.

The Critical Tasks You Will Perform

• You will design and implement efficient training pipelines for foundation models and multimodal models.
• You will develop and optimize model architectures for specific business use cases.
• You will create and implement model compression techniques (quantization, pruning, distillation).
• You will build evaluation frameworks for model performance and quality assessment.
• You will collaborate with cross-functional teams to understand their requirements and integrate foundation models into applications.
• You will engage in code and design reviews with peers to uphold high-standard engineering practices.

What Essential Skills You Will Need

• Your degree in Statistics, Mathematics, Computer Science, or a related field will support your technical insights needed for this role.
• You have over 3 years of experience in machine learning, with focus on deep learning and transformer architectures.
• You have strong theoretical knowledge of recent machine learning developments, including foundational models, transformers, and data-centric AI.
• You have experiences in applying state-of-the-art machine learning architectures to specific problems beyond mere fine-tuning, which are crucial for the role's responsibilities.
• Your have strong programming skills in Python and strong experience with PyTorch or TensorFlow, particularly in training large-scale models.
• You have strong understanding of engineering practices and design patterns, with experience in writing readable, maintainable, and testable code.
• You have good experiences with ML deployment platforms and MLOps.

Life at Grab

We care about your well-being at Grab, here are some of the global benefits we offer:

• We have your back with Term Life Insurance and comprehensive Medical Insurance.
• With GrabFlex, create a benefits package that suits your needs and aspirations.
• Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
• We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
• Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours

What We Stand For at Grab

We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.