Job Description
Are you ready to define the future of Artificial General Intelligence?
Nexus Horizon AI is seeking a visionary Senior AI Architect to lead our breakthrough research in multimodal neural networks. As we accelerate towards the 2026 paradigm shift in AI, you will engineer the core infrastructure powering the next generation of autonomous agents and cognitive systems.
In this pivotal role, you won't just implement existing models; you will architect the foundational layers of intelligence that will define the industry standard for the upcoming decade. You will collaborate with world-class researchers and product engineers to deliver scalable, efficient, and ethical AI solutions that push the boundaries of what is possible.
Why Join Us?
- Work at the forefront of the 2026 AI revolution.
- Competitive equity package and top-tier compensation.
- Flexible remote-first culture with state-of-the-art hardware.
- Opportunity to shape public policy on ethical AI development.
Responsibilities
- Architect and design scalable, high-performance neural network models capable of handling complex, real-time data streams.
- Lead research initiatives focused on LLM optimization, reinforcement learning, and multimodal fusion for 2026 readiness.
- Collaborate with cross-functional teams to translate cutting-edge research into production-grade software architectures.
- Define and implement MLOps pipelines to ensure model reproducibility and deployment efficiency.
- Mentor junior engineers and researchers, fostering a culture of innovation and technical excellence.
- Evaluate and integrate emerging technologies (e.g., Neuromorphic computing, Edge AI) into our core infrastructure.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in AI/ML architecture, with a strong portfolio of published research or deployed models.
- Deep expertise in deep learning frameworks (PyTorch, TensorFlow, JAX) and distributed computing systems.
- Proven track record of optimizing model inference speed and accuracy for large-scale applications.
- Experience with transformer architectures, Large Language Models (LLMs), and generative AI technologies.
- Strong background in software engineering best practices, including CI/CD, containerization (Docker/K8s), and cloud infrastructure (AWS/GCP).