Job Description
Shape the Future of Intelligence.
Nexus Horizon Labs is seeking a visionary Senior Generative AI Architect to lead the development of next-generation autonomous systems. In this pivotal 2026 Vision role, you won't just be maintaining existing models; you will be architecting the foundational infrastructure for the AI evolution of tomorrow. We are looking for a technical leader who thrives at the intersection of theoretical research and scalable engineering.
You will be at the forefront of the AI revolution, deploying multimodal systems that redefine human-computer interaction. If you are passionate about ethical AI, large language models (LLMs), and the trajectory of technology in 2026, this is your opportunity to leave a lasting legacy.
Why Join Us?
- Work with a team of world-class researchers and engineers.
- Competitive compensation package including equity.
- Flexible remote-first culture with top-tier benefits.
- Access to cutting-edge compute resources and proprietary datasets.
Responsibilities
- Lead Model Architecture: Design and implement state-of-the-art generative models, including Large Language Models (LLMs) and diffusion models, tailored for the 2026 landscape.
- Optimization & Efficiency: Drive research into model compression, quantization, and efficient inference to reduce latency and operational costs.
- Multimodal Integration: Build systems capable of seamlessly integrating text, image, audio, and video data for holistic AI understanding.
- RAG & Vector Systems: Develop robust Retrieval-Augmented Generation pipelines to ensure model accuracy and reduce hallucinations.
- Ethical AI Compliance: Implement safety guidelines and bias mitigation strategies to ensure responsible deployment of AI agents.
- Mentorship: Guide a team of junior engineers and data scientists, fostering a culture of innovation and continuous learning.
Qualifications
- Education: MS or PhD in Computer Science, Mathematics, or a related quantitative field.
- Core Expertise: Deep proficiency in Python, PyTorch, and TensorFlow.
- Modeling: Proven experience training and fine-tuning transformer architectures.
- Infrastructure: Experience with cloud platforms (AWS/GCP/Azure) and MLOps tools (MLflow, Kubeflow, Kubernetes).
- Communication: Excellent ability to translate complex technical concepts into strategic roadmaps for stakeholders.
- Future-Forward Mindset: Demonstrated ability to anticipate industry trends and adapt technology stacks for long-term viability.