Job Description
We are at the precipice of a new era in artificial intelligence. As we look toward 2026 and beyond, the definition of intelligent systems is shifting from simple automation to true generative creativity. Nexus Horizon Labs is seeking a visionary Generative AI Architect to lead the charge in building the next generation of Large Language Models (LLMs) and multimodal AI systems.
In this role, you will bridge the gap between theoretical machine learning research and scalable, production-grade engineering. You will architect systems that not only understand context but generate novel, high-fidelity content. If you are passionate about the future of AI and want to define how machines think and create, we want to hear from you.
Why Join Nexus Horizon Labs?
- Shape the Future: Work on foundational AI technologies that will define the landscape of 2026.
- Competitive Compensation: Base salary plus performance-based equity bonuses.
- Elite Team: Collaborate with PhDs and industry veterans from top tech institutions.
- Flexible Environment: Hybrid work model based in our San Francisco innovation hub.
Responsibilities
- Architect and deploy state-of-the-art generative models, including LLMs and diffusion models, optimized for enterprise use cases.
- Design and implement Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy with real-time data.
- Optimize model inference latency and cost-efficiency using techniques like quantization, pruning, and distillation.
- Collaborate with product managers and designers to translate AI capabilities into intuitive user experiences.
- Establish best practices for AI safety, bias mitigation, and ethical deployment of generative technologies.
- Mentor junior engineers and researchers, fostering a culture of continuous learning and innovation.
Qualifications
- Master’s degree in Computer Science, Machine Learning, or a related quantitative field (PhD preferred).
- 5+ years of professional experience in deep learning, NLP, or generative AI development.
- Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of transformer architectures, attention mechanisms, and tokenization strategies.
- Hands-on experience with vector databases (Pinecone, Milvus, Weaviate) and cloud infrastructure (AWS, GCP, Azure).
- Strong background in statistical modeling and experimental design.