Job Description
We are on a mission to define the technological landscape of 2026. At NeuralShift Inc., we aren't just predicting the future; we are architecting it. We are seeking a visionary Senior AI Research Engineer to join our elite Future-Tech division and lead the development of next-generation Generative AI models and autonomous agents.
As part of the 2026 initiative, you will work on cutting-edge problems that bridge the gap between current deep learning capabilities and the sentient systems of tomorrow. You will collaborate with world-class researchers and engineers to build scalable, ethical, and robust AI systems that will power the next decade of innovation.
Why join us?
- Impact: Work on projects that will be integral to the global AI infrastructure.
- Autonomy: Enjoy a high degree of creative freedom in your research directions.
- Equity: Competitive compensation package with significant equity opportunities.
Ready to shape the future? Apply today.
Responsibilities
- Lead the design and implementation of scalable AI architectures for the 2026 roadmap, focusing on Generative Adversarial Networks (GANs) and Large Language Models (LLMs).
- Conduct original research to advance the state-of-the-art in Natural Language Processing and Computer Vision.
- Collaborate with cross-functional teams (Product, Engineering, Ethics Board) to translate research into production-ready applications.
- Define and implement rigorous testing and validation protocols to ensure model reliability, fairness, and safety.
- Mentor junior researchers and engineers, fostering a culture of innovation and continuous learning.
- Present research findings to technical stakeholders and contribute to patent filings.
Qualifications
- PhD or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in AI research or applied machine learning.
- Proven expertise in deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Strong programming skills in Python and C++.
- Deep understanding of transformer architectures, reinforcement learning, and optimization techniques.
- Demonstrated ability to publish in top-tier conferences (NeurIPS, ICML, ICLR) or open-source communities.