Job Description
We are not just building the future; we are defining it. At OmniCorp Future Labs, we are on a mission to engineer the technological landscape of 2026. We are seeking a visionary Senior AI Research Engineer to lead our flagship 2026 Horizon Initiative. In this role, you will architect the neural architectures that will power autonomous systems, generative media, and predictive quantum computing models.
This is a high-impact opportunity for a technical leader who is passionate about pushing the boundaries of Machine Learning, Deep Learning, and Artificial General Intelligence. You will work in a state-of-the-art facility, collaborating with world-class physicists, cryptographers, and software engineers to solve humanity's most complex computational challenges.
Why Join Us?
- Next-Gen Tech Stack: Work with cutting-edge hardware including neuromorphic processors and quantum co-processors.
- Global Impact: Your work will directly influence the trajectory of AI safety and automation in the 2026 era.
- Elite Team: Join a roster of PhDs and industry veterans from top research institutions.
Responsibilities
- Architect and implement scalable neural networks capable of processing multi-modal data streams in real-time.
- Lead the research and development of Generative Adversarial Networks (GANs) and Diffusion Models for synthetic data generation.
- Bridge the gap between classical machine learning algorithms and quantum computing paradigms.
- Develop and maintain rigorous benchmarks for model performance, accuracy, and energy efficiency.
- Collaborate with the Ethics Board to ensure AI alignment and safety protocols for autonomous deployment.
- Mentor junior researchers and data scientists, fostering a culture of innovation and technical excellence.
Qualifications
- PhD or Master’s degree in Computer Science, Artificial Intelligence, Computational Neuroscience, or a related field.
- 7+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing.
- Strong proficiency in Python, C++, and CUDA programming.
- Proven experience with large-scale model training (e.g., Transformers, LLMs) and fine-tuning.
- Deep understanding of statistical learning theory and optimization algorithms.
- Experience deploying AI models in high-throughput, production environments.