Job Description
Are you ready to define the future of intelligence?
Nexus Future Labs is pioneering the next generation of Autonomous Systems and Generative AI. As we accelerate toward our 2026 roadmap, we are seeking a visionary Principal AI Architect to lead the architectural design of our flagship Neural Interface platform.
In this high-impact role, you won't just write code; you will architect the cognitive layer of tomorrow's internet. You will bridge the gap between theoretical breakthroughs and scalable production systems, ensuring our AI remains ethical, efficient, and ahead of the curve.
Why join us?
We offer competitive equity, a remote-first culture, and the chance to work on problems that will define the next decade of human-computer interaction.
Responsibilities
- Architect Next-Gen Systems: Design and implement scalable, fault-tolerant AI infrastructures for Large Language Models (LLMs) and Autonomous Agents.
- R&D Leadership: Spearhead research initiatives focused on multimodal learning and emergent AI behaviors expected by 2026.
- System Optimization: Drive performance tuning and resource optimization for training and inference pipelines at scale.
- AI Safety & Alignment: Develop frameworks to ensure AI outputs adhere to strict ethical guidelines and safety protocols.
- Technical Mentorship: Guide a team of senior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Strategic Planning: Translate business goals into technical roadmaps, identifying emerging technologies that provide a competitive edge.
Qualifications
- Education: PhD in Computer Science, Artificial Intelligence, or a related field, or equivalent extensive experience in AI engineering.
- Technical Expertise: Deep proficiency in Python, PyTorch, and TensorFlow. Extensive experience with Transformer architectures and Hugging Face ecosystems.
- Experience: 8+ years of experience in machine learning engineering, with at least 3 years in a lead or architect role.
- Cloud Mastery: Proven track record deploying ML workloads on AWS, GCP, or Azure using Kubernetes and serverless architectures.
- Problem Solving: Exceptional ability to solve complex, ambiguous problems and debug distributed systems.
- Communication: Excellent verbal and written skills for technical presentations and cross-functional collaboration.