Job Description
We are 2026 Systems, a visionary organization pioneering the next generation of artificial intelligence. We are looking for a visionary Lead AI Engineer to spearhead the development of our flagship 2026 platform. In this role, you will define the technical architecture for our neural networks, ensuring scalability, security, and unprecedented performance. If you are passionate about the future of technology and want to build systems that matter, we want to hear from you.
Why Join 2026 Systems?
- Work on cutting-edge Generative AI and Quantum-Ready algorithms.
- Competitive equity and comprehensive benefits package.
- Flexible remote-first culture with headquarters in the heart of San Francisco.
- Opportunity to mentor a team of world-class engineers.
Responsibilities
- Architectural Leadership: Design and implement scalable, high-performance AI infrastructure capable of handling 2026-scale data volumes.
- Model Development: Lead the research and deployment of proprietary Large Language Models (LLMs) and computer vision systems.
- Technical Strategy: Define the technical roadmap for the 2026 release cycle, integrating emerging technologies like edge computing and federated learning.
- Team Mentorship: Foster a culture of innovation, conducting code reviews, and providing technical guidance to junior and senior engineers.
- Performance Optimization: Continuously monitor system health and optimize models for latency, accuracy, and cost-efficiency.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: 7+ years of experience in software engineering with a specific focus on Machine Learning and Deep Learning.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, and SQL; experience with cloud platforms (AWS/GCP/Azure).
- Leadership: Proven track record of leading technical teams and managing complex project lifecycles from conception to production.
- Problem Solving: Strong analytical skills with the ability to solve ambiguous problems in high-stakes environments.