Job Description
We are on a mission to redefine the boundaries of artificial intelligence. As a Senior Generative AI Engineer at Nebula AI, you won't just be building models; you'll be architecting the cognitive frameworks that will drive enterprise intelligence in 2026 and beyond. We are looking for visionary engineers who are passionate about Large Language Models (LLMs), generative adversarial networks, and the ethical implications of AGI.
Join a team of world-class researchers and engineers dedicated to pushing the frontiers of machine learning. You will have the autonomy to experiment with cutting-edge architectures, optimize inference pipelines, and deploy scalable AI solutions that impact millions of users worldwide.
Why Join Us?
- Work on next-generation LLMs and multimodal AI systems.
- Competitive salary and equity package.
- Flexible remote-first culture with a SF office hub.
- Access to top-tier compute resources and research papers.
Responsibilities
- Design, train, and fine-tune large-scale generative models using Transformer architectures.
- Build and optimize Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and reduce hallucinations.
- Implement rigorous evaluation frameworks to measure model performance, safety, and bias.
- Collaborate with product teams to integrate AI capabilities into scalable, production-grade software.
- Mentor junior engineers and researchers, fostering a culture of innovation and technical excellence.
- Stay abreast of the latest research in Deep Learning and contribute to open-source initiatives.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related field.
- 5+ years of experience in software engineering with a strong focus on Machine Learning or AI.
- Deep expertise in Python, PyTorch, TensorFlow, and Hugging Face Transformers.
- Proven experience deploying LLMs (e.g., GPT-4, Llama 3, Claude) in production environments.
- Familiarity with MLOps tools (MLflow, Kubeflow) and cloud platforms (AWS, GCP, Azure).
- Strong understanding of Natural Language Processing (NLP) concepts and tokenization strategies.