Job Description
Are you ready to architect the intelligent systems of tomorrow? Nexus Future Labs is seeking a visionary Senior AI Systems Architect to spearhead the development of our 2026 roadmap. We are at the forefront of Generative AI, Autonomous Agents, and Quantum-Augmented Computing. In this role, you will design scalable infrastructure that powers the next generation of human-machine interfaces.
We are not just building software; we are defining the trajectory of artificial general intelligence. Join us in San Francisco to build the future.
Responsibilities
- Design Scalable Architectures: Architect and implement high-performance, fault-tolerant inference pipelines for large-scale LLMs (Large Language Models).
- Autonomous Systems: Develop and optimize the architecture for autonomous AI agents capable of complex, multi-step decision-making in dynamic environments.
- Quantum Integration: Collaborate with quantum computing research teams to hybridize classical and quantum algorithms for next-gen problem solving.
- Model Optimization: Apply techniques such as quantization, pruning, and distillation to deploy heavy models on edge devices and cloud infrastructure efficiently.
- Ethical AI Frameworks: Establish and enforce architectural guidelines ensuring transparency, fairness, and safety in AI outputs.
- Technical Leadership: Mentor junior engineers and conduct code reviews to maintain high engineering standards across the team.
- Performance Tuning: Continuously monitor system latency, throughput, and cost-efficiency to optimize cloud resource utilization.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, or a related technical field.
- Core Expertise: Deep understanding of machine learning frameworks, specifically PyTorch, TensorFlow, or JAX.
- System Design: Proven experience designing distributed systems at scale, with a focus on high availability and low latency.
- Programming: Expert proficiency in Python, Go, or Rust, with experience in containerization (Docker/Kubernetes).
- AI Specialization: Hands-on experience with LLMs, RAG (Retrieval-Augmented Generation), and fine-tuning methodologies.
- Problem Solving: Ability to tackle complex mathematical and computational problems in novel ways.
- Communication: Excellent verbal and written communication skills for technical and non-technical stakeholders.