Job Description

Company DescriptionAbout Grab and our workplaceGrab is Southeast Asia\'s leading superapp. We are dedicated to improving the lives of millions of users across the region by providing them everyday services such as deliveries, mobility, financial services, enterprise services and others. More than that, we provide the opportunity for them to have a better life. And that aspiration starts inside Grab because we believe in a seamless blend of work and home life, making every aspect of life better for all.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-we work to create economic empowerment for the people of Southeast Asia. With our unwavering commitment to our values, we believe that we\'re more than a service provider; we\'re agents of positive change.Get to know our TeamGrab 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 RoleThis 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.

QualificationsThe must-havesA 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-havesA Masters or PhD in computer science, machine learning or related fieldsMachine Learning Publications, ideally regarding foundation models.Experience in Golang/RustExperience 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 ModelsContributions to open source projectsAdditional InformationBenefits at Grab:We care deeply about your well-being and are committed to supporting you every step of the way. Here are some of the global benefits we offer:

• Protect and provide for your loved ones with peace of mind, knowing we have your back with Term Life Insurance and comprehensive Medical Insurance.
• Craft a benefits package that suits your unique needs and aspirations with GrabFlex, because we believe in empowering you to thrive.
• Embrace the magic of new life and create lasting memories with your family through Maternity and Paternity Leave.
• Life can be overwhelming, but you\'re never alone. Our confidential Grabber Assistance Programme is here to guide and uplift you and your loved ones through life\'s challenges.
• Your well-being is our priority. Benefit from our holistic well-being initiatives through Wellbeing@Grab, including health programmes, informative webinars, and vibrant carnivals.
• Achieve a harmonious work-life balance with our FlexWork arrangements, allowing you to adapt and thrive in your personal and professional life.

We\'ve got many different benefits hyper localised in each country. Speak to your recruiter during your interview to find out more.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. If you require accommodations to fully participate in the recruitment process, you are encouraged to include your request(s) when applying.We deliver the greatest impact and ideas when we bring together diverse perspectives. It is what enables us to spread opportunities to Grabbers and our partners. It\'s not a box-ticking exercise; it\'s who we are.

Grab