Job Description
Are you ready to architect the next generation of intelligence? Nebula Systems is looking for a visionary Senior AI Engineer to join our elite team in San Francisco. As we prepare for the transformative era of 2026, we need a technical expert who can bridge the gap between theoretical research and scalable, production-grade artificial intelligence.
In this role, you will be at the forefront of Generative AI, Large Language Models (LLMs), and Autonomous Agents. You will work alongside top-tier researchers and engineers to build systems that redefine human-computer interaction. If you are passionate about pushing the boundaries of what is possible in AI and want to leave a lasting legacy in the industry, we want to hear from you.
Responsibilities
- Architect Scalable AI Systems: Design and implement robust, high-performance machine learning pipelines and inference engines capable of handling millions of requests per second.
- Model Optimization: Fine-tune and optimize pre-trained Large Language Models (LLMs) and multimodal models to achieve superior accuracy and reduced latency.
- R&D Leadership: Conduct cutting-edge research on emerging AI paradigms, including Retrieval-Augmented Generation (RAG) and Reinforcement Learning from Human Feedback (RLHF).
- MLOps Integration: Implement CI/CD pipelines for machine learning, ensuring reproducibility and monitoring model performance in real-time production environments.
- Collaborative Innovation: Partner with product managers and data scientists to translate complex business requirements into elegant technical solutions.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in software engineering, with a strong focus on Machine Learning or Artificial Intelligence.
- Core Skills: Proficiency in Python, PyTorch, or TensorFlow; extensive experience with LLMs (e.g., GPT, Llama, Claude architectures).
- System Design: Strong understanding of distributed systems, cloud architecture (AWS/GCP/Azure), and containerization technologies (Docker, Kubernetes).
- Problem Solving: Demonstrated ability to debug complex issues and optimize algorithms for efficiency and scalability.