Research Engineer - World Models

About the Job

Skyfall is building the first enterprise-scale World Model. Our goal is to build a latent world model that gives agents a human-like sense of foresight in complex digital environments. Unlike traditional physical world models for robotics or autonomous driving, which focus on geometry, physics, and low-level control, our model focuses on semantic and predictive structure in tasks such as navigating enterprise software, booking flights, or operating online stores.

We're looking for a Research Engineer (ML) to join our cutting-edge AI research team. This role is ideal for engineers who thrive at the intersection of AI research and scalable software engineering, working on next-generation world models, reinforcement learning, and multi-agent systems. You’ll play a key role in developing AI training infrastructure for world models, and contributing to the broader research community through publications and open-source projects.

Key Responsibilities

Develop Scalable AI Infrastructure – Design and build high-performance training pipelines for world models, multi-modal latent representations, and multi-agent systems. Implement Cutting-Edge AI Techniques – Work with state-of-the-art architectures, including JEPA, transformer models and diffusion models. Optimize AI Model Performance – Collaborate with researchers to improve training efficiency, fine-tuning strategies, and inference optimization for real-world enterprise applications.

Contribute to Research & Open Source – Publish high-impact research, engage with the broader AI community, and contribute to leading open-source AI projects. Work with Large-Scale Systems – Leverage cloud-based GPU environments and distributed computing frameworks to train and deploy large-scale AI models.

Requirements

Bachelor's degree in Computer Science, Machine Learning, or a related technical field. Strong programming skills in Python, with experience in software engineering best practices. Experience with cloud-based GPU training environments (e.g., AWS, Lambda Labs, GCP). Hands-on experience with open-source AI frameworks (e.g.

, Py. Torch, Tensor. Flow, JAX). Experience working with large-scale distributed systems and training pipelines.

Nice to Have

Master’s degree in Computer Science, Machine Learning, or a related technical field. Published research in top AI/ML conferences (e.g., NeurIPS, ICML, ICLR, ACL). Hands-on experience in LLMs, reinforcement learning, or multi-agent systems. Experience optimizing training pipelines for large-scale AI models. Contributions to open-source AI projects or AI research communities.