Job Description
Are you ready to shape the future of intelligence? Nexus Horizon Labs is seeking a visionary Senior AI Architect to spearhead the development of our proprietary 2026 neural engine. We are building the infrastructure that will define the next decade of human-machine interaction, focusing on scalability, ethical alignment, and unprecedented cognitive depth. If you are passionate about the frontier of Artificial General Intelligence (AGI) and want to work in a world-class environment, this is your opportunity.
As a key member of our R&D division, you will bridge the gap between theoretical neuroscience and practical software engineering, creating systems that learn, adapt, and evolve autonomously.
As a key member of our R&D division, you will bridge the gap between theoretical neuroscience and practical software engineering, creating systems that learn, adapt, and evolve autonomously.
Responsibilities
- Architect and implement next-generation neural network architectures optimized for 2026 standards.
- Lead the design of scalable distributed systems capable of processing petabyte-scale datasets in real-time.
- Collaborate with interdisciplinary teams to integrate multimodal AI models (text, vision, audio) into seamless user experiences.
- Establish and enforce rigorous ethical guidelines and safety protocols for autonomous decision-making systems.
- Mentor junior engineers and researchers, fostering a culture of innovation and technical excellence.
- Conduct rigorous performance testing and benchmarking to ensure system reliability under extreme loads.
- Publish research findings and contribute to the open-source community to advance the field of AI.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field with a focus on Deep Learning.
- 8+ years of professional experience in software engineering, with at least 5 years specifically in AI/ML architecture.
- Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of transformer models, reinforcement learning, and causal inference.
- Proven track record of deploying production-grade AI systems at scale.
- Strong background in distributed computing and cloud infrastructure (AWS, GCP, or Azure).
- Excellent communication skills with the ability to translate complex technical concepts for diverse stakeholders.