Lead Machine Learning Engineer, Foundation Models at Kudo

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

23 Jun, 26

Salary

0.0

Posted On

25 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning Engineering, Foundation Models, World Models, Reinforcement Learning, Deep Learning, PyTorch, JAX, Python, Distributed Training, Transformers, Diffusion Models, State-Space Models, LLMs, VLMs, MLOps, Technical Leadership

Industry

Software Development

Description

Company Description 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. Job Description Get to know our Team The AI Automation Team is dedicated to applied research in cutting-edge technologies and the development of highly scalable Foundation World Models that allow our AI systems to understand, simulate, and predict the complex temporal and spatial dynamics of Grab's marketplace across a wide variety of domains. Our core research areas encompass foundation models, embeddings, world models, and reinforcement learning. We are looking for experienced machine learning engineers to join our team and contribute to realizing this vision. Get to know the Role This is an applied research role aimed at developing foundation world model solutions for Grab. We are seeking a visionary Lead Machine Learning Engineer to spearhead our Foundation World Models team. In this role, you will lead the design, pre-training, and adaptation of massive, multi-modal foundation models that simulate physical environments. You will guide a team of ML engineers and researchers to build generalized, foundational architectures capable of zero-shot and few-shot spatio-temporal reasoning and action-conditioned environment simulation. You'll report into the Senior Engineering Manager and be based onsite at Grab One North Singapore office. The Critical Tasks You will Perform Lead the end-to-end development of foundational world models, making critical architectural decisions regarding transformers, diffusion models, state-space models (e.g., Mamba), or latent variable models. Design and optimize distributed training pipelines for massive multimodal data sets using multi-node GPU clusters. Stay at the forefront of AI research. Translate state-of-the-art academic papers in foundation model, world modelling (e.g., Sora, JEPA, Dreamer), and reinforcement learning into scalable, production-ready code. Work with MLOps and systems engineers to optimize model inference, ensuring low-latency and high-throughput for downstream tasks. Collaborate with cross-functional teams to understand their requirements and integrate foundation world models into applications. Mentor team members on foundation world model development and best practices. Qualifications What Essential Skills You Will Need Education: A degree in computer science, artificial intelligence, machine learning, or a related technical field. Experience: At least 6 years of industry experience in ML engineering, with at least 2 years in technical leadership, specifically working on large-scale generative AI or Foundation Models. Foundation Model Expertise: Deep theoretical and practical understanding of self-supervised learning architectures, Large Language Models (LLMs), Vision-Language Models (VLMs), and sequence-to-sequence modeling. Engineering Chops: Expert-level proficiency in Python and deep learning frameworks, specifically PyTorch or JAX. Distributed Systems: Proven track record of training multi-billion parameter models across hundreds or thousands of GPUs using techniques like Ray, FSDP or DeepSpeed. Data Processing at Scale: Experience architecting data pipelines for petabyte-scale training regimens, managing data curation, deduplication, and quality filtering for foundation model pre-training. Additional Information 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.

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Responsibilities

The Lead Machine Learning Engineer will spearhead the Foundation World Models team, leading the design, pre-training, and adaptation of massive, multi-modal foundation models that simulate physical environments. Critical tasks include leading end-to-end development of foundational world models, making architectural decisions, and designing optimized distributed training pipelines for massive multimodal data sets.