Job Description
Are you ready to shape the future of intelligence?
Nexus Future Labs is seeking a visionary Senior Generative AI Engineer to join our elite team in San Francisco. As we accelerate towards our 2026 roadmap, we are building the next generation of Large Language Models (LLMs) and autonomous agents that will redefine human-machine interaction. This is not just a coding role; it is a chance to architect the foundation of tomorrow's technology.
In this high-impact position, you will bridge the gap between cutting-edge research and scalable production systems. You will work directly with our CTO and Lead Researchers to fine-tune models, optimize inference pipelines, and ensure our AI solutions are ethical, efficient, and transformative.
Responsibilities
- Lead Model Development: Design, train, and fine-tune state-of-the-art Generative AI models (e.g., LLaMA, GPT-4 architectures) tailored for enterprise applications.
- Optimization & Scalability: Implement advanced MLOps strategies to reduce latency and inference costs while scaling to millions of users.
- RAG Architecture: Build and maintain robust Retrieval-Augmented Generation pipelines to enhance model accuracy and reduce hallucinations.
- Collaborative Innovation: Partner with product and design teams to translate complex AI capabilities into user-centric features.
- Ethical AI Compliance: Establish and enforce guidelines for responsible AI use, ensuring fairness, transparency, and data privacy.
- Research Integration: Stay ahead of the curve by integrating emerging research findings (e.g., Reinforcement Learning from Human Feedback) into our production stack.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Technical Expertise: 5+ years of experience in software engineering with a strong focus on Deep Learning and NLP.
- Programming: Deep proficiency in Python and experience with frameworks like PyTorch, TensorFlow, or JAX.
- Model Architecture: Solid understanding of Transformer architectures, attention mechanisms, and fine-tuning techniques.
- Infrastructure: Experience deploying models on cloud platforms (AWS, GCP, or Azure) using Docker, Kubernetes, and MLflow.
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems and deliver high-quality, scalable code.