Ifm-us
World Model Lead
Abu Dhabifull timeunspecifiedtechnology
Transformer-based LLMs — Diffusion Models — Reinforcement Learning — World Modeling — Simulation Systems — Hierarchical Latent Representations — Multimodal Data Processing — Production Systems Engineering
Description
Key Responsibilities
Technical Leadership & Vision - Define and evolve the overall world model architecture, drawing on the PAN principles: 1. Multimodal data ingestion 2. Mixed continuous/discrete representations 3. Hierarchical generative modeling with an enhanced LLM backbone and diffusion-based predictors 4. Generative loss grounded in real observations 5. Simulation for RL-based agent training - Establish performance, safety, and evaluation benchmarks, driving continuous improvement.
Project & Team Management - Oversee planning, resourcing, and timeline for world model projects. - Manage research engineers and scientists (e.g., data curators, RL experts, simulator devs) to achieve unified progress.
Cross-Functional Collaboration - Partner with agent, reasoning, and deployment teams to integrate world model outputs into downstream applications (robotics, multi-turn dialogue, autonomous systems). - Liaise with external collaborators (academia & industry) to incorporate the latest advances and tooling.
Governance & Communication - Report project status, risks, and key insights to senior leadership and stakeholders. - Champion best practices in reproducibility, documentation, and knowledge sharing.
Required Qualifications - Ph.D. or M.S. with 8+ years in AI research or engineering, specializing in world modeling, simulation, or generative modeling. - Proven track record building large-scale simulators or predictive models for complex environments. - Deep expertise in transformer-based LLMs, diffusion models, and hierarchical latent representations. - Hands-on experience with reinforcement learning frameworks (policy learning, planning with latent dynamics). - Strong leadership skills: project management, cross-site coordination, and team mentorship.
Preferred Qualifications - Experience leading multi-location technical teams in fast-paced R&D settings. - Published contributions to world model architectures or simulation benchmarks. - Track record of taking research prototypes into production systems.