Job Description
Join the architects of tomorrow.
Nexus Future Labs is at the forefront of the artificial intelligence revolution. We are seeking a visionary Senior Generative AI Engineer to help shape the technological landscape for 2026 and beyond. In this pivotal role, you will design, train, and deploy cutting-edge Large Language Models (LLMs) that push the boundaries of machine intelligence.
Our mission is to create ethical, scalable, and transformative AI systems. You will work closely with our research scientists and product teams to build the next generation of generative applications, ensuring they are robust, efficient, and ready for the future.
Why Join Us?
- Work on the bleeding edge of AI technology.
- Competitive salary and equity package.
- Flexible remote-first culture with a San Francisco office hub.
- Focus on continuous learning and professional growth.
Responsibilities
- Model Architecture & Development: Design and implement novel neural network architectures for Large Language Models, focusing on efficiency and scalability.
- Training & Fine-Tuning: Lead the end-to-end training processes for LLMs using PyTorch and TensorFlow, optimizing for performance and accuracy.
- RAG Implementation: Develop Retrieval-Augmented Generation pipelines to enhance model factual accuracy and reduce hallucinations.
- MLOps & Deployment: Build robust CI/CD pipelines and MLOps infrastructure to deploy models to production environments with minimal latency.
- Ethical AI: Ensure all deployed models adhere to ethical guidelines, bias mitigation strategies, and safety protocols.
- Research & Innovation: Stay ahead of industry trends to anticipate the needs of the AI landscape in 2026.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing.
- Technical Skills: Strong proficiency in Python, PyTorch, and experience with Hugging Face Transformers.
- Infrastructure: Deep understanding of cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Communication: Ability to translate complex technical concepts into actionable insights for non-technical stakeholders.
- Problem Solving: Proven track record of solving complex engineering challenges in high-scale environments.