Job Description
We are building the future of intelligence. Nexus 2026 is a pioneering research lab dedicated to achieving breakthroughs in artificial general intelligence by the target year of 2026. We are seeking a visionary Senior AI Architect to lead our infrastructure team and design the neural frameworks that will power the next generation of global systems.
In this high-impact role, you will not just write code; you will define the architectural standards for a new era of technology. You will work at the intersection of deep learning, scalable cloud infrastructure, and ethical AI implementation.
Why Join Us?
- Work on mission-critical projects that define the trajectory of AI development.
- Competitive compensation and equity packages.
- Flexible remote-first culture with hubs in SF and NYC.
Responsibilities
- Architect and deploy scalable, high-performance neural networks using Python and modern deep learning frameworks (PyTorch, TensorFlow).
- Lead the design and implementation of the 2026 roadmap for our proprietary AI models, ensuring scalability and robustness.
- Optimize model inference and training pipelines to reduce latency and increase throughput on cloud infrastructure (AWS/GCP).
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate research into production-ready applications.
- Establish best practices for code quality, documentation, and version control within the AI research department.
- Mentor junior engineers and data scientists, fostering a culture of continuous learning and innovation.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- 8+ years of professional experience in software engineering and machine learning architecture.
- Deep expertise in Natural Language Processing (NLP) and Large Language Models (LLMs).
- Proven track record of designing systems that handle massive data volumes with high availability.
- Strong proficiency in Python, SQL, and containerization technologies (Docker, Kubernetes).
- Experience with cloud-native services and MLOps tools (MLflow, Kubeflow, Airflow).