About the role
The Company General Intuition We are the frontier research lab dedicated to building foundation models for environments that require deep spatial and temporal reasoning. For the past year, we've been pushing the forefront of AI across agents capable of navigating space and time, world models that provide training environments for those agents, and video understanding models with a focus on transfer to the real world. We raised a seed round of $133M from General Catalyst and Khosla to discover the next generation of intelligence.
What We're Looking For 5+ years of experience in deep learning research or reinforcement learning, with a focus on embodied agents or simulation environments. Strong foundation in representation learning and generative modeling , particularly using architectures such as diffusion models, VAEs, and transformers applied to video. Experience with world models and predictive control - you understand how to train models that simulate dynamics and plan actions in learned environments. Proficiency in reinforcement learning (RL, model-based RL, or imitation learning) and the ability to design and evaluate policy networks. Programming fluency in Python and deep learning frameworks such as PyTorch. Strong experimental skills - comfort with large-scale training, evaluation pipelines, and managing complex datasets or simulations. Publications or open-source contributions in areas like world modeling, simulation learning, or agent policies are a strong plus. In-person: Looking to hire in NYC . 5 days in the office. Ownership & scientific rigor: You see ideas through from concept to proof to deployment. You write clean, reproducible code and maintain a high bar for experimental validity. Performance and scaling mindset: You care about how research translates into production systems, with an understanding of compute efficiency, distributed training, and data bottlenecks. Curiosity-driven and result-oriented: You’re excited by open-ended problems, but you also know how to define measurable goals and ship impactful systems. Gaming & simulation passion: Interest in interactive environments, physics-based simulations, or gaming AI. Experience with Unity, Unreal Engine, or custom simulators is a plus. Our Research Stack Core Research: Python, PyTorch, NumPy, Triton, and CUDA Backend & Infra: Kubernetes, GCP, and large-scale training clusters Experimentation: We run continuous evaluation, A/B testing, and performance metrics tracking on our deployed models