Job Description
We are at the precipice of a new era. Nexus Horizon Systems is seeking a visionary Senior AI Architect to spearhead the 2026 Initiative—a revolutionary project designed to redefine the boundaries of generative intelligence and spatial computing. If you are passionate about building the infrastructure that will power the future of work and play, this is your opportunity to leave a lasting legacy.
In this pivotal role, you will design and deploy scalable machine learning models that will be integrated into the next generation of immersive digital environments. You will work alongside world-class researchers and engineers to solve complex problems in real-time rendering, natural language processing, and adaptive learning systems.
Why Join Us?
- Impact: Directly influence the roadmap for the 2026 platform.
- Compensation: Top-of-market salary and equity package.
- Flexibility: Hybrid work model based in the heart of San Francisco.
Responsibilities
- Architect and optimize end-to-end machine learning pipelines for the 2026 Initiative, focusing on low-latency inference and high-throughput data processing.
- Lead a team of ML engineers in research, development, and deployment of state-of-the-art neural networks using PyTorch and TensorFlow.
- Collaborate with cross-functional teams including product managers, designers, and 3D artists to integrate AI capabilities into user-facing products.
- Mentor junior engineers and establish best practices for code quality, testing, and model governance within the organization.
- Stay ahead of industry trends in AI, specifically within the realms of Large Language Models (LLMs) and Computer Vision.
- Conduct rigorous performance benchmarking and cost analysis to ensure infrastructure scalability.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, Robotics, or a related quantitative field.
- Minimum of 7 years of professional experience in machine learning engineering, with at least 3 years in a senior or lead role.
- Strong proficiency in Python, C++, and GPU programming (CUDA/OpenCL).
- Deep understanding of distributed systems and cloud architecture (AWS, GCP, or Azure).
- Proven track record of deploying large-scale ML models into production environments.
- Excellent communication skills, with the ability to translate complex technical concepts for diverse audiences.