Job Description
About Nexus Horizon Labs: We are a frontier research organization dedicated to defining the technological landscape of the 2026 era. Our mission is to build intelligent systems that are not only robust but adaptable to the rapidly evolving digital world. We are seeking a visionary Senior AI & Machine Learning Engineer to join our elite team in San Francisco and lead the charge in next-generation artificial intelligence.
The Role:
In this high-impact position, you will architect and deploy advanced machine learning models that power our core products. You will be responsible for pushing the boundaries of what is possible with Large Language Models (LLMs), Multi-Agent Systems, and Generative AI. This is a rare opportunity to work on problems that will define the industry standards for the coming years.
Responsibilities
- Architect Next-Gen Models: Design and fine-tune state-of-the-art deep learning architectures, specifically focusing on LLMs and transformer-based models.
- Optimize Inference: Implement efficient inference pipelines and model compression techniques to ensure low-latency performance at scale.
- Research Integration: Translate cutting-edge academic research into production-ready code and scalable infrastructure.
- System Design: Collaborate with backend engineers to integrate AI models into robust, secure, and maintainable software ecosystems.
- Ethical AI: Ensure all models adhere to strict ethical guidelines and safety protocols, mitigating bias and ensuring fairness.
- Team Leadership: Mentor junior engineers and conduct technical code reviews to foster a culture of excellence.
Qualifications
- Education: M.S. or Ph.D. in Computer Science, Mathematics, Statistics, or a related quantitative field.
- Experience: 5+ years of professional experience in building, deploying, and maintaining Machine Learning systems in a production environment.
- Programming: Expert-level proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow.
- Core Skills: Deep understanding of Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), and Vector Databases.
- Cloud Native: Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).