Job Description
We are seeking a visionary Lead AI Researcher to architect the intelligent systems that will define the year 2026. At Nebula Dynamics, we don't just predict the future; we build it. You will be at the forefront of developing next-generation Large Language Models (LLMs) and autonomous agents designed to revolutionize enterprise operations. This is a high-impact role for someone who thrives on ambiguity and wants to leave a lasting legacy in the tech landscape.
As a key architect on our 2026 roadmap, you will bridge the gap between theoretical research and production-grade engineering. You will lead a world-class team of researchers and engineers, setting the technical direction for our AI infrastructure. If you are passionate about pushing the boundaries of what is possible in artificial intelligence and want to shape the digital landscape of tomorrow, we want to hear from you.
Responsibilities
- Lead the architectural design and implementation of proprietary AI models targeting the 2026 deployment cycle.
- Drive research initiatives in Generative AI, Reinforcement Learning, and Multi-Agent Systems.
- Optimize model performance, scalability, and inference latency for real-time applications.
- Mentor and cultivate a high-performance engineering culture within the research division.
- Collaborate with product leadership to translate complex AI capabilities into user-centric features.
- Establish and enforce best practices for data privacy, security, and ethical AI usage.
Qualifications
- PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
- 8+ years of experience in machine learning research and engineering, with at least 3 years in a leadership or architectural capacity.
- Deep expertise in Python, PyTorch, TensorFlow, and distributed computing frameworks.
- Proven track record of publishing in top-tier AI conferences (NeurIPS, ICML, ACL) or shipping high-scale ML products.
- Experience with fine-tuning LLMs and developing RAG (Retrieval-Augmented Generation) pipelines.
- Strong understanding of system design principles, including Kubernetes, Docker, and cloud infrastructure (AWS/GCP).