About the Job:
Skyfall AI is on a mission to build the future of autonomous enterprises, and we're looking for a Research Software Development Engineer (RSDE) 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 language models, reinforcement learning, and multi-agent systems. You’ll play a key role in developing AI training infrastructure, pushing the boundaries of LLMs and RL, 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 language models, reinforcement learning agents, and multi-agent systems.

Implement Cutting-Edge AI Techniques - Work with state-of-the-art architectures, including transformer models, reinforcement learning frameworks, and generative AI techniques.

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.

Robust 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.

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📌 Research Engineer ML (Ontario)
🏢 Socket.dev
📍 Ontario