Job Description
Are you ready to shape the technology landscape of 2026 and beyond?
Nexus Future Labs is seeking a visionary AI Systems Architect to lead the development of scalable, next-generation artificial intelligence infrastructure. In this pivotal role, you won't just write code; you will define the architectural blueprints for the future of machine learning and automated intelligence.
We are looking for a leader who thrives in ambiguity and is passionate about pushing the boundaries of what is possible. If you are ready to architect the systems that will define the next era of technology, we want to hear from you.
Why Join Us?
- Work on cutting-edge AI models and generative technologies.
- Competitive salary and equity package.
- Flexible remote-first policy with a hub in San Francisco.
- Unlimited PTO and professional development budget.
Responsibilities
- Design and implement high-level architectural systems for AI models, deep learning pipelines, and large language models (LLMs).
- Lead the technical vision for the '2026 Roadmap,' ensuring scalability, reliability, and performance for future workloads.
- Collaborate with cross-functional teams (Data Science, Product, Engineering) to integrate AI solutions into core products seamlessly.
- Mentor junior engineers and senior staff alike, establishing coding standards and architectural best practices for future-proof development.
- Conduct rigorous code reviews and architectural audits to ensure system integrity and security compliance.
- Drive the adoption of cloud-native technologies and containerization strategies (Kubernetes/Docker) to optimize infrastructure costs.
Qualifications
- 5+ years of experience in software architecture, with a specific focus on AI, Machine Learning, or Deep Learning.
- Strong proficiency in Python, C++, or Rust, with deep understanding of data structures and algorithms.
- Experience with distributed systems, cloud infrastructure (AWS, GCP, or Azure), and high-availability system design.
- Proven track record of integrating LLMs into production environments and optimizing inference latency.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.
- Experience with Agile methodologies and leading engineering teams.