Job Description
We are not just building the future; we are defining it. Nebula Horizon Labs is at the forefront of the Artificial Intelligence revolution, developing the autonomous systems and generative models that will power the global economy in 2026 and beyond. We are looking for a visionary Senior Generative AI Architect to lead our research and engineering teams in building the next generation of Large Language Models (LLMs) and multimodal AI systems.
In this role, you will bridge the gap between cutting-edge research and scalable production infrastructure. You will be responsible for designing robust architectures, optimizing model inference, and ensuring ethical AI practices are embedded into our core systems. If you are passionate about the potential of AGI (Artificial General Intelligence) and want to leave a lasting impact on the world, we want to hear from you.
Responsibilities
- Architect and deploy state-of-the-art generative models, including LLMs and diffusion models, optimized for low-latency, high-volume production environments.
- Lead the research and implementation of novel techniques in prompt engineering, fine-tuning, and reinforcement learning from human feedback (RLHF).
- Design scalable data pipelines for training and evaluation, ensuring data integrity and diversity.
- Collaborate with cross-functional teams of researchers, engineers, and product managers to translate theoretical advancements into practical applications.
- Establish and enforce best practices for AI safety, bias mitigation, and ethical AI governance.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- Minimum of 5+ years of professional experience in AI/ML engineering, with at least 2 years focused on Generative AI or NLP.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Strong understanding of deep learning architectures, transformer models, and optimization techniques.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Proven track record of shipping production-level machine learning systems.
- Excellent communication skills with the ability to explain complex technical concepts to diverse audiences.