AMI Engineer - Singapore

Requirements

• Bachelor's degree or equivalent experience in Computer Science or a related field
• Proficiency in Python
• Ability to design, run, and analyze experiments independently
• Understanding of machine learning fundamentals, large-scale training, and accelerator-based (GPU or TPU) compute environments
• Deep expertise in at least one of the following: self-supervised learning, video and multimodal model architectures, planning algorithms
• Demonstrated record of contributing to advanced research projects via publications and/or major model releases
• Experience developing evaluation frameworks for world models
• Experience releasing and maintaining open-source projects
• Proficiency in a deep learning framework (PyTorch or JAX)

Additional Information

About AMI We are building a new breed of AI systems that (1) understand the real world, (2) have persistent memory, (3) can reason and plan, and (4) are controllable and safe. We are a team of scientists and engineers building frontier world model-based AI. We combine the scientific rigor of a top-tier research institute with focus on engineering excellence and execution. We are a global company, with offices in Paris, Montreal, New York, and Singapore. Come build the future of AI with us! AMI believes AI agents should predict and plan using an internal model of the world - their world model. We're looking for new team members to advance the state-of-the-art in world modeling. We believe that video is a rich and abundant source of data reflecting how the world works, and that in general models need to be able to process continuous, high-dimensional data from a variety of sensors to: (a) understand context about the current state of the physical world, (b) make predictions about how the world will evolve, possibly as a result of actions taken, and (c) plan and adapt sequences of actions to complete complex tasks, possibly in dynamic, complex environments. You will work with a team on world model research efforts, including: Self-supervised learning methods to efficiently learn from video and other continuous, high-dimensional signals New architectures that efficiently learn to predict world dynamics from video and other high-dimensional signals Scalable algorithms for pre-processing and curating video data Evaluations for benchmarking world model understanding, prediction, and planning Efficient algorithms for model-based planning and reasoning

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