Job Description
We are on a mission to define the landscape of Artificial Intelligence for the 2026 era. At Nebula Future Systems, we are building the next generation of autonomous agents and generative models that will revolutionize human-computer interaction. We are looking for a visionary Senior AI Research Scientist to lead our core research division.
In this role, you will not just be writing code; you will be architecting the future. You will work at the intersection of deep learning, natural language processing, and ethical AI frameworks to solve problems that currently seem impossible. If you are passionate about pushing the boundaries of what machines can learn and create, we want to hear from you.
Responsibilities
- Lead Research Initiatives: Spearhead the design and implementation of novel neural architectures, specifically focusing on large language models (LLMs) and multimodal transformers.
- Model Optimization: Drive research into model compression, quantization, and inference optimization to deploy state-of-the-art models on edge devices.
- Technical Mentorship: Guide a team of junior researchers and data scientists, fostering a culture of innovation, curiosity, and technical excellence.
- Cross-Functional Collaboration: Partner with product engineering teams to translate theoretical research into scalable, production-ready software solutions.
- Publication & Patenting: Publish high-impact research papers at top-tier conferences (NeurIPS, ICML, ICLR) and secure intellectual property for the company.
- Ethical AI Oversight: Ensure all models adhere to strict ethical guidelines regarding bias, fairness, and transparency in AI decision-making processes.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related field with a focus on AI/ML.
- Experience: Minimum of 5 years of industry experience in machine learning research and development, with at least 2 years in a leadership or senior technical role.
- Technical Proficiency: Strong command of Python, PyTorch, or TensorFlow, with deep understanding of distributed computing frameworks.
- Research Track Record: Proven track record of publishing research in top-tier peer-reviewed venues and contributing to open-source ML communities.
- Domain Knowledge: Deep expertise in NLP, computer vision, or reinforcement learning, with a keen interest in future-forward technologies (e.g., AGI pathways, quantum-inspired algorithms).
- Communication: Excellent verbal and written communication skills, capable of presenting complex technical concepts to both technical and non-technical stakeholders.