Job Description
We are redefining the boundaries of artificial intelligence for the year 2026 and beyond. At Nexus Horizon Systems, we are not just building software; we are architecting the sentient infrastructure of tomorrow. We are seeking a visionary Senior AI Architect to lead our core research division in San Francisco. If you are passionate about the next generation of generative AI, quantum-ready algorithms, and the ethical implementation of AGI, this is your opportunity to shape the future.
As a key leader in our technical team, you will bridge the gap between theoretical research and scalable production systems. You will be responsible for designing the neural architectures that will power the next decade of global connectivity.
Why Join Us?
- Impact: Work on projects that will define the technological landscape of 2026 and beyond.
- Innovation: Access to cutting-edge hardware and proprietary datasets.
- Equity: Competitive stock options in a high-growth startup environment.
Responsibilities
- Lead the architectural design and implementation of next-generation neural networks and deep learning models.
- Define the technical roadmap for 2026 AI infrastructure, ensuring scalability, security, and efficiency.
- Collaborate with cross-functional teams to integrate AI solutions into complex enterprise ecosystems.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Optimize model inference latency and reduce computational costs for real-time applications.
- Stay ahead of industry trends in AI ethics, quantum computing, and edge AI deployment.
- Oversee the deployment of Generative AI tools that drive business transformation.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- 10+ years of experience in software engineering and machine learning architecture.
- Deep expertise in Python, PyTorch, TensorFlow, and distributed computing frameworks.
- Proven track record of deploying large-scale AI models to production environments.
- Strong understanding of MLOps, cloud infrastructure (AWS/GCP/Azure), and containerization (Docker/K8s).
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.
- Experience with reinforcement learning and large language models (LLMs).