Job Description
Are you ready to define the trajectory of Artificial General Intelligence (AGI) by 2026? Nexus Future Systems is seeking a visionary Lead AI Architect to spearhead our next-generation generative AI initiatives. We are building the infrastructure that powers tomorrow's autonomous systems, and we need a technical leader with a passion for pushing the boundaries of what's possible.
In this pivotal role, you will design the architecture for Large Language Models (LLMs), optimize inference pipelines, and ensure our AI solutions are scalable, secure, and ethically aligned. If you are a thought leader looking to make a tangible impact on the future of technology, we want to hear from you.
Why Join Us?
- Work on cutting-edge AGI research and deployment.
- Competitive equity package and top-tier compensation.
- Flexible remote-first culture with a San Francisco HQ.
- Access to the latest hardware for AI training and inference.
Responsibilities
- Design and architect scalable, fault-tolerant AI infrastructure for training and deploying state-of-the-art LLMs.
- Lead the research and implementation of novel deep learning algorithms to improve model accuracy and efficiency.
- Collaborate with cross-functional teams (Product, Engineering, Ethics) to translate business requirements into technical AI roadmaps.
- Optimize model inference latency and reduce computational costs using techniques like quantization and model distillation.
- Establish best practices for MLOps, data governance, and AI safety within the organization.
- Mentor senior engineers and guide technical strategy for the AI research team.
Qualifications
- PhD or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- 10+ years of experience in software engineering with a focus on Machine Learning and Deep Learning.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Proven track record of deploying production-ready AI models at scale.
- Strong understanding of Natural Language Processing (NLP) and Generative AI architectures.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).