Job Description
At Nexus Future Labs, we are not just building software; we are architecting the intelligence layer of the next decade. As we approach 2026, the demand for robust, scalable, and ethical AI systems is at an all-time high. We are seeking a visionary AI Systems Architect to lead the design and implementation of our next-generation autonomous agent infrastructure.
In this pivotal role, you will bridge the gap between cutting-edge Machine Learning research and production-grade distributed systems. You will be responsible for ensuring our platforms can handle millions of concurrent reasoning tasks while maintaining the highest standards of safety and efficiency. If you want to work on the bleeding edge of technology and define the standards for AI in 2026, we want to hear from you.
Why Nexus Future Labs?
- Work with a team of PhD researchers and industry veterans.
- Competitive equity and a salary package reflecting your expertise.
- Flexible remote-first culture with a flagship office in the heart of SF.
Ready to build the future? Apply today.
Responsibilities
- Design Scalable Architectures: Architect end-to-end systems for LLM orchestration and autonomous agent workflows capable of scaling to enterprise levels.
- Infrastructure Optimization: Implement high-performance inference pipelines and optimize model serving for latency and throughput reduction.
- Research Translation: Translate theoretical research breakthroughs into stable, production-ready software components.
- Risk Management: Establish guardrails and safety protocols to ensure AI outputs align with ethical guidelines and regulatory standards.
- Cross-Functional Leadership: Mentor junior engineers and collaborate closely with data scientists and product managers to define technical roadmaps.
- Cloud Strategy: Drive the migration and management of AI workloads on cloud-native environments (AWS/GCP).
Qualifications
- Experience: 5+ years of software engineering experience with at least 2 years specifically focused on Machine Learning systems or Deep Learning infrastructure.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, and modern C++ for performance-critical components.
- Systems Knowledge: Deep understanding of distributed systems, containerization (Docker/Kubernetes), and microservices architecture.
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field (PhD preferred).
- AI Expertise: Demonstrated experience with Large Language Models (LLMs), RAG (Retrieval-Augmented Generation), and prompt engineering frameworks.
- Soft Skills: Exceptional problem-solving abilities and the ability to communicate complex technical concepts to non-technical stakeholders.