Job Description
Shape the Intelligence of 2026
Nexus AI Labs is a frontier research company dedicated to defining the future of Artificial General Intelligence. We are looking for a visionary Lead Generative AI Engineer to spearhead the development of next-generation multimodal models. You will be responsible for designing architectures that push the boundaries of what is possible with Large Language Models (LLMs) and generative diffusion systems.
Why This Role?
- Be a Pioneer: Work on core technology that will define the industry standard for the next decade.
- Impact at Scale: Deploy models that power millions of interactions globally.
- Top-Tier Compensation: Competitive base salary plus significant equity opportunities.
Join us in San Francisco to build the systems of tomorrow.
Responsibilities
- Model Architecture: Design and implement state-of-the-art generative models, focusing on Transformers, diffusion models, and reinforcement learning.
- System Optimization: Architect MLOps pipelines to ensure high-throughput training and low-latency inference in production environments.
- Research & Innovation: Conduct deep-dive research to improve model reasoning, reduce hallucinations, and enhance multilingual capabilities.
- Tech Leadership: Mentor junior engineers, conduct code reviews, and establish engineering best practices within the AI team.
- Product Integration: Collaborate closely with product managers to translate complex AI capabilities into intuitive user features.
- Ethical AI: Implement safety guardrails and bias mitigation strategies to ensure responsible AI deployment.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related technical field.
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX. Deep understanding of transformer architectures.
- Infrastructure: Experience with cloud platforms (AWS/GCP/Azure) and containerization technologies (Docker, Kubernetes).
- Problem Solving: Strong analytical skills with a proven track record of optimizing model performance and scalability.