Job Description
We are on the cusp of a new technological era. At Nexus Future Labs, we are defining the 2026 Horizon of artificial intelligence—focusing on autonomous agents, next-generation neural architectures, and sustainable deep learning models. We are seeking a visionary Senior AI Architect to lead our core research and engineering team in San Francisco.
In this role, you will not just build models; you will architect the future of human-machine interaction. You will work with state-of-the-art compute infrastructure to develop the next generation of LLMs and multi-modal systems that will power industries for years to come. If you are passionate about pushing the boundaries of what is possible in AI and want to shape the standards of 2026, we want to meet you.
Why Join Us?
- Work with the most advanced hardware stack available today and tomorrow.
- Competitive equity package and performance bonuses tied to model breakthroughs.
- Flexible remote-first culture with a hub in the heart of SF.
Responsibilities
- Design 2026-Ready Architectures: Spearhead the design of scalable, efficient, and robust neural network architectures capable of handling complex, multi-modal data streams.
- Agentic AI Implementation: Lead the development of autonomous AI agents that can plan, execute, and learn from complex, long-horizon tasks without human intervention.
- Optimization & Efficiency: Implement aggressive quantization, pruning, and distillation techniques to reduce inference costs while maintaining model accuracy.
- Research Leadership: Publish high-impact research papers and contribute to open-source communities to establish Nexus Future Labs as a thought leader in the 2026 AI landscape.
- Model Evaluation: Establish rigorous benchmarks and evaluation frameworks to ensure model safety, fairness, and alignment with human values.
- Technical Mentorship: Mentor junior architects and engineers, fostering a culture of continuous learning and technical excellence.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 8+ years of professional experience in building and deploying large-scale machine learning systems.
- Technical Skills: Deep expertise in PyTorch, TensorFlow, or JAX with a strong command of distributed training frameworks (Ray, Kubernetes).
- Mathematical Proficiency: Strong background in Linear Algebra, Calculus, Probability, and Statistics.
- Software Engineering: Excellent software engineering practices, including CI/CD, testing, and code reviews.
- Soft Skills: Exceptional communication skills with the ability to translate complex technical concepts for cross-functional teams.