Job Description
We are seeking a visionary Senior AI Research Engineer to join our elite team in San Francisco. At Nexus Future Labs, we are pioneering the next generation of Artificial Intelligence and Generative Models. If you are passionate about pushing the boundaries of what is possible with Large Language Models (LLMs) and neural architectures, this is your opportunity to shape the future of technology.
As a key member of our R&D division, you will work on cutting-edge projects that redefine industry standards. We offer a competitive compensation package, equity options, and a collaborative environment where innovation is encouraged. You will be at the forefront of the AI revolution, building systems that understand, reason, and create.
Responsibilities
- Design and implement state-of-the-art Machine Learning algorithms and deep neural network architectures.
- Lead research initiatives focused on Generative AI, NLP, and Computer Vision to solve complex business problems.
- Collaborate with cross-functional teams to integrate AI models into scalable, production-grade software environments.
- Optimize model performance, inference efficiency, and accuracy for real-world applications using techniques like quantization and distillation.
- Stay abreast of the latest academic papers and industry trends to drive continuous innovation and maintain a competitive edge.
- Mentor junior engineers and researchers, fostering a culture of technical excellence and continuous learning.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, Statistics, or a related technical field.
- Deep expertise in Python, PyTorch, or TensorFlow with a strong understanding of the underlying mathematics.
- Proven experience with Large Language Models (e.g., GPT, BERT, Llama) and fine-tuning techniques.
- Strong understanding of Deep Learning principles, Transformers, and Neural Networks.
- Excellent problem-solving skills and the ability to work autonomously in a fast-paced, agile environment.
- Experience with cloud platforms (AWS, GCP, or Azure) and MLOps pipelines is a plus.