Model-Based RL Scientist for Multi-Agent Systems

Pheratech Systems, Inc.; Los Angeles (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Pheratech Systems, Inc. in Los Angeles, CA is seeking a Research Scientist to advance model-based reinforcement learning for coordinated robotic systems. You will work with the research lead on world models, consensus, and networked dynamics, with ownership over research questions and direction.

The role emphasizes learning objective design, experimentation, and collaboration with simulation staff to connect scientific advances to robotic coordination across timescales.

Qualifications

• PhD in machine learning, robotics, CS, applied mathematics, physics, control, or related.
• Post-PhD research experience of at least one year in academia or industry.
• Strong publication record with original scientific contributions.
• Deep expertise in model-based RL and learning dynamics.
• Expertise in multi-agent representations and decentralized execution.
• World modeling, state estimation, and planning under uncertainty.
• Strong statistical validation, experimental design, and ablations.
• Excellent technical communication and collaborative proposal writing.
• Awareness of experimental best practices and rigorous record-keeping.

Responsibilities

• Originate research questions and develop novel algorithms and representations.
• Develop world models and model-based RL for interacting, heterogeneous agents.
• Investigate communication, partial observability, changing teams, and coupled dynamics.
• Develop uncertainty-aware exploration and planning with limited data.
• Explore hierarchical and temporal representations across timescales.
• Adapt small VLMs and connect multimodal grounding to world models and planning.
• Design rigorous experiments to test hypotheses and compare explanations.
• Collaborate with researchers and simulation engineers to apply advances to robotics.
• Lead projects through implementation, validation, and scientific publication.
• Attend conferences/workshops and assist with proposals.

Skills

Model-based RL

Multi-agent representations

World modeling

State estimation

Planning under uncertainty

Latent representations

Small VLMs

Multimodal grounding

Hierarchical representations

Temporal representations

Python

PyTorch

JAX

Scientific communication

Proposal writing

Experimental design

Uncertainty analysis

Education

PhD in ML/CS/related

Tools

PyTorch

JAX

Job description

Pheratech Systems, Inc. in Los Angeles, CA is seeking a Research Scientist to advance model-based reinforcement learning for coordinated robotic systems. You will work with the research lead on world models, consensus, and networked dynamics, with ownership over research questions and direction.

The role emphasizes learning objective design, experimentation, and collaboration with simulation staff to connect scientific advances to robotic coordination across timescales.