The Role
As a member of technical staff, you will lead the core engineering effort to build compute efficient, physically accurate world models. Your work will enable our AIs to "dream" and predict how the world evolves, and help engineers evaluate and debug physical AI systems in real-like environments.
You are responsible for supporting the development cycle of the World Model, from training to deployment. You have a deep understanding of the technology stack, and you are capable of delivering feedback, and contribution at each and every layer.
Job Responsibilities
- Area: Design and scale the core software stack, bridging the gap between cloud infrastructure (orchestration/tooling) and model internals (training/inference).
- Ownership: The architectural scaling and technical delivery of production-grade world foundation models.
- Focus: Building high-performance distributed infrastructure (addressing memory/compute bottlenecks) and physically grounded verification loops.
- Operations: Establishing the engineering "Rule of Law"—standardizing reproducibility, compute ROI, and a culture of clarity across the research team.
Minimum Qualifications
- Extensive experience in software engineering for deep learning R&D teams.
- Expert-level software development experience with deep learning frameworks (e.g., PyTorch, JAX) and large-scale distributed training infrastructures.
- Expert with Python, C++ / Rust.
Preferred Qualifications
- Proof of outstanding contribution to the open source libraries for deep learning training.
- Peer-reviewed research contribution in deep learning, from a AAA conference.
- Comfortable with CUDA programming.