Job Description
Are you ready to define the future of intelligence? Nebula AI Systems is seeking a visionary Senior AI Engineer to join our elite team in San Francisco. In this role, you will lead the development of next-generation Large Language Models (LLMs) and deploy scalable, high-performance machine learning solutions that redefine user experiences.
We are not just building models; we are architecting the brain of the next digital era. You will work at the intersection of deep research and production engineering, optimizing models for real-time inference and ensuring robust, ethical data pipelines. If you are passionate about pushing the boundaries of Generative AI and MLOps, this is your opportunity to make a significant impact.
Why Join Nebula AI?
- Work with state-of-the-art technology including Transformers, GNNs, and Reinforcement Learning from Human Feedback (RLHF).
- Competitive compensation package with equity options.
- Flexible remote-first policy with a premium office in downtown San Francisco.
- Access to top-tier computing resources and a collaborative culture of innovation.
Responsibilities
- Design, train, and fine-tune state-of-the-art Generative AI models and Large Language Models (LLMs).
- Architect and optimize MLOps pipelines for continuous training, validation, and deployment.
- Collaborate closely with data scientists and product managers to translate research into production-ready software.
- Implement best practices for model monitoring, explainability, and governance to ensure safety and reliability.
- Conduct cutting-edge research to push the boundaries of Natural Language Processing (NLP) and multimodal understanding.
- Drive technical decisions regarding infrastructure scaling and cost optimization for large-scale training jobs.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, Statistics, or a related field (or equivalent experience).
- 5+ years of professional experience in Machine Learning and AI engineering, with a focus on deep learning frameworks.
- Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep understanding of LLM architectures, transformers, attention mechanisms, and fine-tuning methodologies.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- Proven track record of deploying models to high-traffic production environments.