Job Description
We are at the precipice of a technological revolution. Nexus Horizon Labs is seeking a visionary Senior AI Architect to spearhead our 'Project 2026' initiative, a groundbreaking framework designed to redefine the boundaries of artificial general intelligence and autonomous systems.
In this role, you will not just build algorithms; you will architect the digital infrastructure of the future. You will collaborate with a world-class team of quantum physicists, neuroscientists, and software engineers to create scalable, ethical, and robust AI solutions that will shape the next decade.
Why Join Us?
- Work on cutting-edge projects that define the next era of technology.
- Competitive compensation package with equity options.
- Flexible remote-first culture with access to state-of-the-art research facilities.
If you are ready to lead the charge into 2026 and beyond, we want to hear from you.
Responsibilities
- Design and deploy scalable, high-performance machine learning infrastructure capable of processing petabytes of real-time data.
- Lead the research and implementation of novel neural network architectures and generative AI models.
- Define technical roadmaps for AI product development, ensuring alignment with long-term strategic goals.
- Mentor junior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Ensure the ethical integrity and transparency of AI models, adhering to global regulatory standards.
- Collaborate cross-functionally with product managers and engineering teams to translate complex requirements into technical solutions.
- Optimize existing models for deployment on edge devices and cloud environments.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, Mathematics, or a related quantitative field.
- 10+ years of experience in software engineering and machine learning, with at least 5 years in a senior leadership role.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Proven track record of designing end-to-end AI systems that have been deployed in production environments.
- Strong understanding of distributed systems, cloud computing (AWS, GCP, or Azure), and containerization (Docker/Kubernetes).
- Excellent communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.
- Familiarity with ethical AI frameworks, bias mitigation, and responsible AI practices.