The Role
You will own the end to end strategy, design, and delivery of our general-purpose world modeling efforts. You ll translate cutting-edge research (e.g., the PAN framework) into robust, production-ready simulators, guide a multidisciplinary team of engineers and scientists, and ensure alignment with IFM s mission.
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.
