Job Description
Are you ready to define the future of Artificial Intelligence?
Nexus Horizon Technologies is seeking a visionary Lead AI/ML Architect to join our elite team in San Francisco. As we accelerate towards our 2026 strategic roadmap, we are looking for a technical pioneer to architect scalable, next-generation machine learning systems that will power the next era of digital transformation.
In this role, you will not just write code; you will shape the foundational infrastructure of our AI ecosystem. You will bridge the gap between theoretical research and production-grade deployment, ensuring our models are robust, ethical, and ready for the challenges of 2026 and beyond.
Why Join Us?
• Work on cutting-edge projects that redefine industry standards.
• Competitive compensation package and equity opportunities.
• Access to the latest hardware and cloud infrastructure.
• Collaborative environment with top-tier talent.
If you are passionate about the trajectory of AI and want to leave a lasting impact, we want to hear from you.
Responsibilities
- Architect and deploy scalable AI/ML infrastructure designed for the 2026 landscape, including large language models and generative AI workflows.
- Lead the end-to-end machine learning lifecycle, from data ingestion and feature engineering to model training, evaluation, and productionization.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate business requirements into technical solutions.
- Optimize model performance for speed and accuracy, implementing best practices in MLOps and DevOps.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Ensure compliance with ethical AI guidelines and data privacy regulations.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, Mathematics, or a related field.
- Minimum of 7+ years of experience in software engineering and machine learning.
- Deep proficiency in Python, PyTorch, TensorFlow, or JAX.
- Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Proven track record of deploying high-impact machine learning models in production environments.
- Excellent communication skills and the ability to articulate complex technical concepts to non-technical stakeholders.