Job Description
Are you ready to build the intelligence infrastructure of tomorrow?
At Nexus Horizon Labs, we aren't just predicting the future; we are architecting it. We are seeking a visionary Lead AI Architect to spearhead the development of the next generation of Artificial General Intelligence (AGI). If you are passionate about pushing the boundaries of what is possible in 2026 and beyond, this is your stage.
In this role, you will lead a high-performance team of researchers and engineers to design scalable, robust, and ethical AI systems that redefine human-machine interaction. You will operate at the intersection of deep learning, distributed systems, and cognitive science.
Why Join Us?
- Work on cutting-edge AGI research.
- Competitive compensation and equity packages.
- Flexible remote-first culture with a hub in San Francisco.
- Access to state-of-the-art hardware and cloud infrastructure.
Responsibilities
- Architect Scalable Systems: Design and implement robust, fault-tolerant AI infrastructures capable of handling billions of parameters and real-time inference at scale.
- R&D Leadership: Drive research initiatives in large-scale language models (LLMs), transformer architectures, and reinforcement learning to achieve AGI milestones.
- Model Optimization: Lead efforts in model compression, quantization, and edge deployment to ensure high efficiency across various hardware environments.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and security experts to translate complex research into deployable products.
- Mentorship: Cultivate a high-performing engineering culture by mentoring junior architects and conducting technical workshops.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 8+ years of experience in software engineering with a heavy emphasis on machine learning and AI systems.
- Technical Stack: Proficiency in Python, C++, Rust, or Go; extensive experience with PyTorch, TensorFlow, or JAX.
- System Design: Deep understanding of distributed systems, microservices, and cloud-native architectures (AWS, GCP, or Azure).
- Problem Solving: Proven track record of solving complex, open-ended problems in AI research or production environments.