Job Description
We are at the precipice of the 2026 AI revolution. Nexus Horizon Systems is seeking a visionary Lead AI Architect to design the foundational models and intelligent agents that will define the next decade of human-computer interaction. You will be responsible for pushing the boundaries of Generative AI, Large Language Models (LLMs), and Multimodal systems.
In this role, you won't just be maintaining legacy systems; you will be building the infrastructure for the future. We are looking for a technical leader who thrives in ambiguity and is obsessed with the scalability, safety, and performance of state-of-the-art AI models.
Why Join Us?
- Work on cutting-edge projects that bridge the gap between theoretical AI research and production-grade deployment.
- Competitive compensation packages and equity opportunities in a high-growth startup.
- Flexible remote-first culture with headquarters in the heart of San Francisco.
Responsibilities
- Architect and optimize Large Language Model (LLM) pipelines, including pre-training, fine-tuning, and RLHF.
- Design Retrieval-Augmented Generation (RAG) architectures to enhance model accuracy and reduce hallucinations.
- Lead the implementation of MLOps best practices for continuous model training and deployment.
- Collaborate with cross-functional teams (Product, Engineering, Data Science) to integrate AI capabilities into consumer applications.
- Establish frameworks for AI safety, ethics, and bias mitigation in production environments.
- Stay ahead of industry trends, specifically focusing on developments expected to shape the 2026 landscape.
Qualifications
- Master’s degree or Ph.D. in Computer Science, Artificial Intelligence, or a related technical field.
- 7+ years of experience in Machine Learning, Deep Learning, or NLP with a focus on Generative AI.
- Expert proficiency in Python, PyTorch, or TensorFlow.
- Proven experience fine-tuning models using Hugging Face, LoRA, or PEFT techniques.
- Strong understanding of vector databases (e.g., Pinecone, Milvus) and RAG architectures.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).