Job Description
We are Nexus Horizon, a premier research lab pioneering the technologies that will define the year 2026 and beyond. We are looking for a visionary AI/ML Architect to lead the development of next-generation Generative AI systems. If you are passionate about pushing the boundaries of neural architectures and ethical AI, we want to meet you.
In this role, you will not just build models; you will define the architecture of intelligence for the future. You will work alongside world-class researchers to deploy scalable, high-performance AI solutions that solve complex global challenges.
Why Join Us?
- Work on cutting-edge AGI research.
- Competitive equity and stock options.
- Flexible remote-first policy with a premium office in SF.
- Access to the latest hardware and compute resources.
Ready to architect the future? Apply today.
Responsibilities
- Design and Deploy: Architect and implement state-of-the-art machine learning models, focusing on scalability and real-time inference capabilities.
- Research Leadership: Lead internal research initiatives exploring emerging paradigms in Neural Networks and Large Language Models.
- System Optimization: Optimize model training pipelines and reduce inference latency to ensure seamless user experiences.
- Ethical AI: Ensure all deployed models adhere to strict ethical guidelines and fairness standards.
- Collaboration: Work cross-functionally with software engineers and product managers to translate research into production-ready products.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related technical field.
- Experience: 5+ years of professional experience in AI/ML engineering, with at least 2 years in a lead or architect role.
- Technical Skills: Deep expertise in Python, PyTorch, or TensorFlow. Experience with distributed training frameworks (Ray, Kubernetes).
- Specialization: Proven track record in Natural Language Processing (NLP) or Computer Vision.
- Problem Solving: Ability to troubleshoot complex system bottlenecks and optimize deep learning infrastructure.