Job Description
We are seeking a visionary Senior Generative AI Engineer to join our elite research and development team in San Francisco. At Nexus Future Labs, we are building the next generation of intelligent systems that redefine human-computer interaction. You will be at the forefront of the Generative AI revolution, working on cutting-edge Large Language Models (LLMs) and multimodal systems.
If you are passionate about pushing the boundaries of artificial intelligence and have a deep understanding of machine learning architectures, we want to hear from you. This is an opportunity to shape the future of technology while enjoying a competitive compensation package and a dynamic, remote-first culture.
Why Join Us?
- Work with state-of-the-art tools and proprietary frameworks.
- Competitive equity and benefits package.
- Flexible remote work policy and professional development budget.
Responsibilities
- Design, train, and fine-tune large-scale generative models (LLMs) using transformer architectures.
- Implement and optimize Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and reduce hallucinations.
- Collaborate with cross-functional teams of researchers, engineers, and product managers to translate AI capabilities into user-centric products.
- Conduct rigorous experimentation and evaluation of model performance, including benchmarking against industry standards.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Ensure model scalability, latency efficiency, and cost-effectiveness in production environments.
- Stay abreast of the latest academic research and industry trends in Generative AI to integrate novel techniques.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related technical field.
- 5+ years of professional experience in Machine Learning, Deep Learning, or AI Engineering.
- Extensive experience with Python, PyTorch, or TensorFlow.
- Strong proficiency in working with Hugging Face Transformers, LangChain, or similar libraries.
- Deep understanding of NLP, Natural Language Understanding, and text generation algorithms.
- Proven track record of deploying AI models into production environments.
- Excellent problem-solving skills and the ability to thrive in a fast-paced, ambiguous startup environment.