Job Description
Shape the Intelligence of Tomorrow.
We are seeking a visionary Senior AI Engineer to join Nexus Future Labs and architect the next generation of Generative AI systems. As we prepare to redefine the technological landscape for 2026 and beyond, we need a pioneer who thrives on pushing the boundaries of what is possible with Large Language Models (LLMs) and autonomous agents.
In this role, you won't just maintain existing systems; you will build the core infrastructure that powers our future products. You will work with a world-class team of researchers and engineers to solve complex problems in reasoning, multi-modal synthesis, and real-time learning.
Why Join Us?
- Work on cutting-edge research with direct commercial impact.
- Competitive compensation and equity packages.
- Flexible remote-first culture with a hub in the heart of San Francisco.
Responsibilities
- Design and implement scalable Generative AI architectures tailored for 2026 standards of efficiency and accuracy.
- Optimize model inference latency and reduce computational costs using quantization and pruning techniques.
- Lead the development of Retrieval-Augmented Generation (RAG) pipelines to enhance knowledge base reliability.
- Collaborate with product teams to translate advanced research into deployable AI features.
- Mentor junior engineers and conduct code reviews to maintain high engineering standards.
- Experiment with novel architectures such as state-space models or hybrid neural-symbolic systems.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related technical field (or equivalent practical experience).
- 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing.
- Strong proficiency in Python, PyTorch, or TensorFlow, with deep knowledge of CUDA and GPU optimization.
- Extensive experience working with Transformer models, LLMs, and Hugging Face ecosystem.
- Proven track record of shipping production-level AI models.
- Experience with MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS, GCP).