Ami
AMI Engineer
3mo ago
Europeself-supervised learningvideo processingmodel optimization
Develop and optimize AI systems that understand and predict real-world data using video and sensor information.
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
Nice to have
- Strong track record of building and deploying high-performance ML models
- Experience developing, testing, and maintaining large-scale distributed systems
- Experience releasing and maintaining open-source projects
- Proficiency in a deep learning framework (PyTorch or JAX), especially for distributed training and efficient inference
Other
- 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 of scientists and engineers, including:
- Implementing and optimizing robust and scalable self-supervised learning methods to efficiently learn from video and other continuous, high-dimensional signals
- Develop and scale new architectures that efficiently learn to predict world dynamics from video and other high-dimensional signals, focusing on performance and efficiency
- Scalable infrastructure and algorithms for pre-processing and curating video data
- Efficient algorithms for model-based planning and reasoning