Job Description
Are you ready to architect the future of intelligence by 2026? At Quantum Horizon, we are not just building AI; we are defining the trajectory of technology for the next decade. We are seeking a visionary Lead AI Architect (2026 Vision) to spearhead our research and development initiatives.
In this high-impact role, you will bridge the gap between theoretical machine learning breakthroughs and scalable production systems. You will be responsible for designing the neural architectures that will power our products in the year 2026 and beyond. If you are passionate about pushing the boundaries of what is possible in Generative AI and Large Language Models (LLMs), we want to meet you.
Why join us?
- Work on cutting-edge projects that define the 2026 tech landscape.
- Competitive compensation package and equity options.
- Flexible remote-first culture with headquarters in San Francisco.
- Access to state-of-the-art hardware and research libraries.
Responsibilities
- Design and implement scalable AI architectures with a focus on future-proofing for 2026 standards.
- Lead a team of data scientists and engineers to develop next-generation Natural Language Processing models.
- Collaborate with product teams to translate futuristic concepts into tangible product features.
- Conduct rigorous research to explore emerging AI paradigms, including Neuromorphic computing integration.
- Mentor junior architects and researchers, fostering a culture of innovation and excellence.
- Optimize model performance and reduce inference costs for global deployment.
- Stay ahead of industry trends to ensure Quantum Horizon remains the leader in AI innovation.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field with a focus on AI/ML.
- Minimum of 8+ years of experience in machine learning, deep learning, or artificial intelligence engineering.
- Proven experience leading architecture discussions and technical roadmaps for complex systems.
- Deep expertise in Python, PyTorch, TensorFlow, and distributed computing frameworks.
- Strong understanding of LLMs, Transformer architectures, and fine-tuning methodologies.
- Excellent communication skills with the ability to articulate complex technical concepts to non-technical stakeholders.
- Demonstrated ability to work in a fast-paced, agile environment.