Job Description
Are you ready to define the architecture of the next decade? Nexus Horizon AI is recruiting a visionary Senior AI Architect to lead Project 2026, our flagship initiative to revolutionize generative intelligence and autonomous systems.
In this pivotal role, you will not just build models; you will engineer the underlying infrastructure that powers the next generation of human-computer interaction. You will work alongside world-class researchers and engineers to solve the most complex scalability and safety challenges in AI.
Why join us?
• Impact: Work on a "moonshot" project with a $2B runway.
• Culture: Top-tier talent, equity compensation, and flexible remote-first culture.
• Tools: Access to the latest hardware (H100 clusters) and open-source libraries.
Key Objectives:
Drive the architectural vision for Project 2026, ensuring robust, secure, and scalable deployment of large language models across edge and cloud environments.
Responsibilities
- Design and implement the core distributed systems architecture for Project 2026, focusing on low-latency inference and high-throughput training pipelines.
- Lead technical strategy for model serving, including containerization, orchestration (Kubernetes), and edge deployment strategies.
- Collaborate with research scientists to bridge the gap between theoretical models and production-grade software engineering.
- Define and enforce best practices for code quality, security, and monitoring in a high-stakes environment.
- Mentor junior engineers and architects, fostering a culture of continuous learning and innovation.
- Conduct rigorous performance benchmarking and optimization to reduce computational costs by up to 40%.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related technical field.
- 8+ years of experience in software engineering, with a focus on AI/ML systems and distributed systems.
- Deep proficiency in Python, C++, and Rust, with experience in frameworks like PyTorch or TensorFlow.
- Proven track record of deploying large-scale machine learning models into production environments.
- Strong understanding of GPU architecture and high-performance computing clusters.
- Excellent communication skills, with the ability to translate complex technical concepts for diverse stakeholders.