Job Description
Are you ready to define the trajectory of artificial intelligence in 2026 and beyond? Nexus Horizon Labs is seeking a visionary Senior AI Systems Architect to lead the engineering of our next-generation autonomous reasoning engines. We are building the cognitive layer of the digital economy, and we need a technical leader who thrives on ambiguity and possesses a deep understanding of scalable machine learning infrastructure.
In this pivotal role, you will bridge the gap between theoretical AI research and production-grade software engineering, ensuring our systems are robust, ethical, and ready for the future of computing. Join us in shaping the future of work, creativity, and intelligence.
Why Join Nexus Horizon Labs?
- Work on projects that define the industry standard for AI safety and scalability.
- Competitive compensation package with equity options.
- Flexible remote-first culture with a premium office in San Francisco.
- Access to cutting-edge hardware and proprietary datasets.
Responsibilities
- Architecting Neural Infrastructures: Design and implement high-performance distributed systems for training and inference of Large Language Models (LLMs) and multi-modal AI agents.
- System Optimization: Drive technical excellence by optimizing model latency, throughput, and resource utilization to support billions of daily interactions.
- R&D Leadership: Conduct research into emerging paradigms such as Federated Learning, Edge AI, and Quantum-Ready AI architectures.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and security experts to translate complex AI capabilities into user-centric products.
- Code Review & Mentorship: Establish and enforce rigorous coding standards, conduct technical code reviews, and mentor junior engineers to foster a culture of technical excellence.
- Ethical AI Implementation: Lead initiatives to ensure model transparency, fairness, and alignment with human values.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 10+ years of software engineering experience, with at least 5 years specifically focused on AI/ML systems architecture.
- Technical Stack: Proficiency in Python, C++, or Rust; deep experience with frameworks such as PyTorch, TensorFlow, or JAX.
- Infrastructure: Proven track record of deploying AI systems on cloud platforms (AWS, GCP, or Azure) using Kubernetes and microservices.
- Problem Solving: Demonstrated ability to solve complex, large-scale system problems and make architectural trade-offs under tight deadlines.
- Communication: Exceptional verbal and written communication skills, with the ability to explain complex technical concepts to diverse stakeholders.