Job Description
Are you ready to architect the future of intelligence?
Nexus Horizon Labs is seeking a visionary Senior AI Architect to lead our 2026 Horizon Initiative. As we stand on the precipice of the next era of artificial general intelligence, we need a pioneer to design the systems that will define the 2026 landscape. You won't just be maintaining legacy models; you will be building the foundational infrastructure for autonomous agents, sentient interfaces, and next-generation neural networks.
In this role, you will bridge the gap between theoretical research and practical application, ensuring our products are not just functional today, but revolutionary tomorrow. Join a team of elite engineers and data scientists working in a high-performance environment where innovation is the only metric that matters.
Why Join Nexus Horizon?
- Impact: Shape the trajectory of AI technology for the global market.
- Equity: Competitive equity package tied to company milestones.
- Flexibility: Hybrid work model (3 days in-office in SF).
- Growth: Unlimited learning budget and access to cutting-edge compute.
Responsibilities
- Architect 2026-Ready Systems: Design and implement scalable, secure, and high-performance AI infrastructure capable of handling the computational demands of the 2026 era.
- Research & Development: Lead R&D efforts in Generative AI, Multi-modal learning, and Agentic workflows to stay ahead of industry trends.
- Model Optimization: Oversee the fine-tuning and deployment of Large Language Models (LLMs) and neural architectures to ensure maximum inference efficiency.
- Technical Leadership: Mentor junior engineers and data scientists, conducting code reviews and architectural reviews to maintain high engineering standards.
- Cross-Functional Collaboration: Partner with product managers and designers to translate complex technical requirements into user-centric AI solutions.
- Publish & Present: Contribute to the academic community by publishing patents and whitepapers on emerging AI methodologies.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: 7+ years of professional experience in machine learning, deep learning, or systems engineering.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, and distributed computing frameworks (e.g., Kubernetes, Ray).
- AI Expertise: Deep understanding of Transformer architectures, Reinforcement Learning, and Natural Language Processing (NLP).
- Problem Solving: Demonstrated ability to solve complex, unstructured problems with innovative technical solutions.
- Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.