Job Description
Join the Pioneers of the Future
Nebula Horizon Technologies is seeking a visionary Senior AI Architect to lead our strategic initiative for 2026 and beyond. In this pivotal role, you will define the architectural roadmap for next-generation Artificial Intelligence systems, focusing on Agentic AI, Autonomous Decision Making, and Ethical Frameworks. If you are passionate about shaping the technological landscape of tomorrow, we want to hear from you.
Why Join Us?
- Work on cutting-edge projects that redefine industry standards.
- Competitive compensation package with equity options.
- Flexible hybrid work environment in the heart of San Francisco.
Responsibilities
- Architectural Leadership: Design scalable, high-performance AI infrastructures capable of handling complex 2026-era data demands.
- R&D Strategy: Lead research initiatives into emerging AI paradigms, including Large Language Models (LLMs) and Neural Symbolic AI.
- System Integration: Oversee the seamless integration of AI models into production environments, ensuring robust security and compliance.
- Team Mentorship: Mentor junior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Product Vision: Collaborate with product managers to translate complex technical concepts into market-ready solutions.
- Ethical AI: Establish and enforce guidelines for responsible AI development and bias mitigation.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related technical field.
- Experience: 7+ years of experience in AI/ML engineering, with at least 2 years in a senior or lead architectural role.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and distributed computing systems.
- Strategic Thinking: Demonstrated ability to think long-term (5-10 year horizon) regarding technology trends and market shifts.
- Communication: Exceptional ability to communicate complex technical ideas to non-technical stakeholders.
- Problem Solving: Proven track record of solving ambiguous, high-stakes problems in dynamic environments.