Job Description
Are you ready to architect the next generation of intelligent systems? Neural Nexus AI is seeking a visionary Senior AI Engineer to lead the development of cutting-edge Large Language Models (LLMs) and generative AI solutions. We are pushing the boundaries of artificial intelligence to solve complex real-world problems, and we need a technical leader who is passionate about the intersection of data science and scalable engineering.
In this role, you will own the technical strategy for our core AI infrastructure, working directly with our research scientists and product teams to deploy models that are not only accurate but also efficient and secure. You will be responsible for the full lifecycle of our models, from research and training to fine-tuning and deployment in production environments.
Why join us?
- Work on state-of-the-art AI technology with a competitive salary and equity package.
- Flexible remote-first culture with a hub in the heart of San Francisco's tech scene.
- Access to top-tier hardware for compute-intensive model training.
Responsibilities
- Model Architecture & Development: Design, train, and optimize large-scale deep learning models, with a focus on Transformers and generative architectures.
- System Optimization: Implement advanced optimization techniques (e.g., quantization, pruning, distillation) to improve inference speed and reduce latency.
- MLOps Pipeline: Build robust, scalable MLOps pipelines using tools like Kubernetes, Docker, and MLflow to manage the deployment and monitoring of models.
- RAG & Fine-tuning: Develop Retrieval-Augmented Generation (RAG) systems and fine-tune open-source models (e.g., Llama, Mistral) for specific domain applications.
- Research Integration: Translate academic research papers into production-ready code and stay ahead of the curve in the rapidly evolving AI landscape.
- Cross-Functional Collaboration: Partner with product managers and engineers to define AI requirements and deliver high-impact features to our users.
Qualifications
- Education: Master’s or PhD degree in Computer Science, Mathematics, Statistics, or a related field (PhD preferred).
- Experience: 5+ years of professional experience in machine learning, deep learning, or natural language processing.
- Programming: Strong proficiency in Python, PyTorch, or TensorFlow.
- Frameworks: Deep familiarity with Hugging Face Transformers, LangChain, and distributed training frameworks (e.g., DeepSpeed, Ray).
- Tools: Experience with cloud platforms (AWS/GCP/Azure) and containerization technologies (Docker, Kubernetes).
- Math: Solid foundation in linear algebra, calculus, and probability/statistics.