Job Description
We are Nebula Future Systems, a pioneer in next-generation artificial intelligence infrastructure. As we prepare to launch our flagship 2026 platform, we are seeking a visionary Senior AI Architect to lead the design and deployment of our autonomous agent ecosystem.
In this role, you won't just be maintaining legacy systems; you will be architecting the future of human-machine collaboration. You will define the technical standards for the 2026 era, ensuring our AI models are scalable, ethical, and transformative. Join us in building the technology that will define the next decade of innovation.
Why Join Us?
- Work on cutting-edge Generative AI and Agentic workflows.
- Competitive compensation package with equity for long-term impact.
- Flexible remote-first culture with access to premium San Francisco amenities.
Ready to shape the future? Apply today.
Responsibilities
- Architect Infrastructure: Design and implement scalable AI/ML infrastructure pipelines capable of handling petabyte-scale data for the 2026 roadmap.
- Model Optimization: Lead the optimization of Large Language Models (LLMs) for latency and accuracy, ensuring real-time inference capabilities.
- R&D Leadership: Drive research initiatives into emerging AI paradigms, including Multi-Agent Systems and Reinforcement Learning.
- System Design: Oversee the architectural integrity of our core platforms, ensuring high availability and fault tolerance.
- Mentorship: Cultivate a high-performance engineering culture by mentoring junior developers and defining technical best practices.
- Cross-Functional Collaboration: Partner with product managers and data scientists to translate business goals into technical roadmaps.
- Ethical AI: Implement guardrails and safety protocols to ensure AI deployment aligns with ethical standards.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field (PhD preferred).
- Experience: 7+ years of experience in software engineering with a focus on AI/ML systems.
- Programming: Expert proficiency in Python, with deep knowledge of PyTorch or TensorFlow.
- Cloud Mastery: Strong experience deploying models on AWS, GCP, or Azure using containerization (Docker/Kubernetes).
- MLOps: Demonstrated experience in building and maintaining MLOps pipelines.
- Problem Solving: Proven track record of solving complex engineering challenges in high-scale environments.
- Communication: Excellent ability to communicate complex technical concepts to non-technical stakeholders.