Research Engineer - World Models

Job summary

Develop scalable AI infrastructure and high-performance training pipelines for enterprise-scale world models. Implement and optimize state-of-the-art AI architectures while contributing to research publications and open-source projects.

Job details

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. Minimum Qualifications: 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., PyTorch, TensorFlow, JAX). Experience working with large-scale distributed systems and training pipelines. Nice to Have Qualifications: 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.

What you’ll do

Develop scalable AI infrastructure and high-performance training pipelines for enterprise-scale world models. Implement and optimize state-of-the-art AI architectures while contributing to research publications and open-source projects.

Requirements

Requires a Bachelor's degree in Computer Science or Machine Learning with strong Python skills and experience in cloud-based GPU environments. Proficiency with AI frameworks like PyTorch or JAX and experience with large-scale distributed systems are essential.

Listed skills

• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• PyTorch
• TensorFlow
• JAX
• Distributed Computing
• Reinforcement Learning
• Multi-Agent Systems
• Transformer Models
• Diffusion Models
• JEPA
• Cloud GPU Training
• Software Engineering
• AI Infrastructure
• Model Optimization
• Latent Representations

Job areas

• Software
• Technology
• Science & Research
• Engineering
• Data & Analytics