Get to know the Role
This is an applied research role aimed at building foundational models for Grab. Responsibilities include proposing deep learning architectures and constructing scalable data and model pipelines.
The ideal candidate should have a strong background in modern machine learning techniques, including pretraining, multi-modality, reinforcement learning, and optimization. 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 day-to-day activities
- You will stay updated with the latest machine learning research to incorporate state-of-the-art techniques into our models.
- You will translate complex business problems into effective machine learning solutions, driving impactful results.
- You will propose, design, develop, and launch advanced machine learning architectures suited to Grab’s unique needs.
- You will oversee the end-to-end lifecycle of machine learning applications, ensuring robustness and scalability.
- You will design and implement algorithms to extract deep insights and trends from extensive, complex datasets.
- You will participate in code and design reviews, maintaining high engineering standards.
- You will mentor junior engineers, offering constructive feedback and support to foster their growth.
Qualifications
The must-haves
- A degree in Computer Science, Software Engineering, Information Technology, or related fields.
- Strong theoretical knowledge of recent machine learning developments, including foundational models, transformers, and data-centric AI.
- Experience in applying state-of-the-art machine learning architectures to specific problems beyond mere fine-tuning.
- Proficient in translating business problems into ML/AI projects.
- Strong understanding of engineering practices and design patterns, with experience in writing readable, maintainable, and testable code.
- Proficiency in ML frameworks and tools such as TensorFlow, PyTorch, MLFlow, W&B, etc.
- Proficiency in distributed training frameworks like Ray and Deepspeed.
- Proficiency in any big data framework like Spark, and familiarity with real-time event processing.
- Experience in developing production-quality ML pipelines.
The nice-to-haves
- A Masters or PhD in computer science, machine learning or related fields
- Machine Learning Publications, ideally regarding foundation models.
- 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.).
- Experience with Large Language Models
- Contributions to open source projects
