Posted 3 months ago
Research Engineer - World Models
@ Skyfall AI
Toronto, Ontario
Responsibilities:
- Develop Scalable AI Infrastructure – Design and build high-performance training pipelines for world models, multi-modal latent representations, and…
- 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…
- 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 summary:
- 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.
Tools & skills
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-08-10
- First seen by our source
- 2026-11-11
- Source record updated
- 2026-11-11
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Skyfall AI
| Field | Value |
|---|---|
| Open jobs on Jobsy | 5 |
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.
Journal
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