Job Description
We are seeking a visionary Senior Generative AI Engineer to join our elite research division in San Francisco. At Nexus Future Systems, we are not just building the technology of today; we are architecting the intelligent systems of 2026 and beyond. If you have a passion for Large Language Models (LLMs), Computer Vision, and ethical AI development, we want to hear from you.
As a key member of our team, you will lead the development of next-generation AI agents capable of complex reasoning, autonomous decision-making, and seamless human-AI collaboration. You will work in a fast-paced, innovative environment with top-tier talent, pushing the boundaries of what is possible in artificial intelligence.
Why Join Us?
- Work on projects that define the future of human-computer interaction.
- Competitive compensation package with equity options.
- Flexible remote-first culture with a vibrant SF hub.
- Access to cutting-edge hardware and research facilities.
Responsibilities
- Design, train, and fine-tune large-scale generative models using transformer architectures.
- Lead the end-to-end deployment of AI models into production environments, ensuring high scalability and performance.
- Research novel algorithms to improve model efficiency, accuracy, and reduce hallucinations.
- Collaborate with cross-functional teams including product managers, data scientists, and designers to translate technical requirements into user-centric features.
- Establish best practices for AI ethics, data privacy, and model explainability.
- Mentor junior engineers and researchers, fostering a culture of continuous learning and innovation.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
- Minimum of 5+ years of experience in machine learning and deep learning engineering.
- Strong proficiency in Python, PyTorch, or TensorFlow.
- Extensive experience working with LLMs (GPT, BERT, Llama) and RAG architectures.
- Proven track record of deploying machine learning models to production at scale.
- Experience with MLOps tools (Docker, Kubernetes, MLflow) and cloud platforms (AWS, GCP, or Azure).
- Excellent problem-solving skills and the ability to work in a highly collaborative, fast-paced environment.