Job Description
We are seeking a visionary Lead AI Architect to spearhead the engineering of our flagship 2026 Model, a next-generation large language model designed to redefine human-machine interaction. As a pioneer in the field of artificial general intelligence, Apex Neural Systems is building the infrastructure for tomorrow. In this role, you will bridge the gap between theoretical research and scalable production deployment, ensuring the 2026 Model delivers unparalleled performance, safety, and efficiency.
Why Join Us?
- Work on the most advanced AI infrastructure in the world.
- Competitive compensation and equity packages.
- Flexible remote-first culture with a hub in San Francisco.
- Opportunity to shape the future of AI safety and alignment.
Responsibilities
- Model Architecture: Design and implement the core architecture for the 2026 Model, focusing on transformer efficiency and multimodal capabilities.
- Performance Optimization: Oversee the training pipeline, optimizing inference speed and reducing computational costs through aggressive quantization and pruning.
- Research Integration: Translate cutting-edge academic research into production-ready code, integrating novel techniques into the 2026 Model ecosystem.
- Infrastructure Management: Lead the engineering team in managing large-scale GPU clusters and distributed training environments.
- Code Review & Mentorship: Establish rigorous code quality standards and mentor junior engineers and researchers to foster a culture of excellence.
- Deployment: Ensure seamless deployment of the 2026 Model across cloud and edge environments with high availability.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related technical field.
- Experience: 5+ years of experience in building large-scale machine learning systems, specifically with LLMs or deep learning frameworks.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow, with deep knowledge of C++ for performance-critical components.
- System Design: Demonstrated experience in designing distributed systems and high-throughput inference pipelines.
- Knowledge: Strong understanding of NLP, attention mechanisms, and model fine-tuning methodologies (LoRA, QLoRA).
- Communication: Excellent verbal and written communication skills, with the ability to articulate complex technical concepts to diverse stakeholders.