Job Description
Are you ready to engineer the future?
Nexus Horizon is pioneering the technology stack for 2026. We are seeking a visionary Senior AI & Quantum Readiness Engineer to lead the architectural transition into next-generation computing paradigms. If you are passionate about scalable systems, neural networks, and preparing infrastructure for the quantum leap, this is your moment to shape the digital landscape.
In this role, you will bridge the gap between classical machine learning and future-proof architectures, ensuring our platforms are robust, efficient, and ready for the demands of the next decade.
Why Join Nexus Horizon?
- Work at the forefront of AI innovation with a team of world-class engineers.
- Competitive compensation and equity packages for top-tier talent.
- Flexible remote-first culture with a hub in the heart of San Francisco.
- Access to cutting-edge hardware and research libraries.
Responsibilities
- Architect Future-Proof Systems: Design scalable infrastructure that integrates classical AI models with quantum-ready protocols, ensuring seamless transition and operation.
- Optimize Neural Architectures: Lead the research and implementation of advanced deep learning models, focusing on latency reduction and throughput maximization.
- Cloud & Edge Integration: Oversee the deployment of containerized AI solutions across hybrid cloud environments (AWS/Azure) and edge computing nodes.
- Performance Tuning: Conduct rigorous stress testing and benchmarking to prepare systems for high-volume loads anticipated in 2026.
- Team Leadership: Mentor junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
- Security & Compliance: Implement robust security protocols to protect sensitive AI training data and proprietary algorithms.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field (PhD preferred).
- Experience: 5+ years of professional experience in AI engineering, Machine Learning Ops (MLOps), or Systems Architecture.
- Programming: Expert proficiency in Python, C++, and experience with quantum computing libraries (Qiskit, Cirq) is a major plus.
- Frameworks: Deep understanding of TensorFlow, PyTorch, and Kubernetes for model deployment.
- Problem Solving: Proven track record of solving complex system design challenges in high-stakes environments.
- Communication: Exceptional ability to translate complex technical concepts for cross-functional stakeholders.