Job Description
The Future is Here. Nexus 2026 is pioneering the next generation of artificial intelligence and autonomous systems. We are looking for a visionary Lead AI Architect to join our elite engineering team in San Francisco. In this role, you will define the architectural framework for our upcoming suite of generative AI products, ensuring scalability, security, and innovation.
We are not just building software; we are architecting the intelligence that will define the next decade of human-machine interaction. If you are passionate about pushing the boundaries of what is possible with Deep Learning and Neural Networks, we want to hear from you.
Responsibilities
- Architectural Vision: Design and implement robust, scalable AI infrastructure for large-scale machine learning systems and generative models.
- Leadership: Lead a high-performing team of data scientists and ML engineers, providing technical mentorship and fostering a culture of innovation.
- Model Optimization: Drive the optimization of LLMs and neural networks for improved inference speed, latency, and cost-efficiency.
- System Integration: Collaborate with cross-functional product teams to integrate AI capabilities into consumer-facing applications seamlessly.
- R&D: Stay ahead of the curve on emerging AI trends, researching novel architectures like Transformers, Graph Neural Networks, or reinforcement learning agents.
Qualifications
- Education: MS or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: 8+ years of experience in software engineering, with at least 5 years specializing in Machine Learning and Deep Learning.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, and experience with cloud platforms (AWS, GCP, or Azure).
- Leadership: Proven track record of leading technical teams and managing complex project lifecycles from conception to deployment.
- Problem Solving: Exceptional ability to solve complex, unstructured problems and design systems that can handle high concurrency and data volume.