Job Description
Are you ready to architect the future of technology?
Nexus Horizon Labs is at the forefront of innovation, pioneering the breakthroughs that will define the year 2026. We are looking for a visionary Senior AI Architect to lead our research into autonomous agents, quantum-augmented machine learning, and synthetic data ecosystems.
In this role, you will bridge the gap between theoretical AI research and scalable production systems. You will work directly with the CTO and lead a team of elite engineers to deploy systems that are not just intelligent, but adaptable, secure, and resilient.
Why Join Us?
- Future-Ready Tech Stack: Work with cutting-edge frameworks including PyTorch, JAX, and proprietary quantum simulators.
- Impact: Your work will set the standard for AI infrastructure for the next decade.
- Compensation: Competitive base salary and equity package.
We are looking for a builder who thinks ahead of the curve.
Responsibilities
- Lead Architecture: Design and implement high-throughput, low-latency AI inference pipelines capable of handling petabyte-scale data streams.
- Agentic AI: Develop the core logic for autonomous AI agents that can plan, execute, and self-correct complex multi-step workflows.
- Quantum Integration: Explore and prototype hybrid quantum-classical algorithms to optimize specific machine learning workloads.
- Model Optimization: Implement pruning, quantization, and knowledge distillation techniques to deploy massive models on edge devices.
- Mentorship: Cultivate a culture of technical excellence by mentoring junior engineers and conducting code reviews.
- Research: Stay ahead of the curve by publishing in top-tier conferences and reading the latest pre-prints on generative AI.
Qualifications
- Education: MS or PhD in Computer Science, Artificial Intelligence, Mathematics, or a related quantitative field.
- Experience: 5+ years of professional experience in Machine Learning Engineering, with at least 2 years in a leadership or senior architect role.
- Core Skills: Deep proficiency in Python, PyTorch, TensorFlow, and distributed computing systems (Kubernetes, Docker).
- AI Knowledge: Extensive experience with Large Language Models (LLMs), fine-tuning (LoRA/PEFT), and Retrieval-Augmented Generation (RAG).
- Mathematical Aptitude: Strong understanding of linear algebra, calculus, and probability theory.
- Communication: Excellent verbal and written communication skills for technical documentation and stakeholder presentations.