Job Description
Are you ready to define the architecture of tomorrow?
Nexus Future Systems is seeking a visionary AI Evolution Architect to lead our cutting-edge initiatives. In this high-impact role, you will bridge the gap between theoretical AI breakthroughs and scalable, production-grade technology. We are not just building for today; we are engineering the foundational systems that will power the next decade of intelligent automation.
You will work at the forefront of the 2026 niche, collaborating with world-class engineers and researchers to deploy state-of-the-art models. If you thrive in a fast-paced, innovative environment and possess a deep understanding of neural architectures, we want to hear from you.
Why Join Us?
- Work on projects that directly shape the future of human-computer interaction.
- Competitive compensation package with equity options.
- Flexible remote-first culture with a vibrant San Francisco hub.
Responsibilities
- Design & Architecture: Lead the end-to-end design of large-scale AI systems, ensuring scalability, security, and performance for future growth.
- Model Optimization: Drive the optimization of deep learning models for latency, throughput, and resource efficiency in edge environments.
- Technical Leadership: Mentor a team of senior engineers and data scientists, conducting code reviews and architectural reviews.
- Integration Strategy: Define the roadmap for integrating proprietary AI models with legacy infrastructure and third-party APIs.
- R&D Collaboration: Work closely with research teams to translate academic breakthroughs into commercial applications.
- Future-Proofing: Continuously assess emerging technologies (e.g., Neuromorphic computing, Quantum AI interfaces) and integrate relevant advancements into our stack.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in AI/ML engineering, with at least 2 years in a senior or lead architect role.
- Technical Skills: Proficiency in Python, PyTorch, or TensorFlow; experience with MLOps tools (Kubernetes, MLflow, Airflow).
- Domain Knowledge: Deep understanding of Natural Language Processing (NLP) and Generative AI models.
- Problem Solving: Exceptional ability to solve complex, unstructured problems and design robust system architectures.
- Communication: Strong written and verbal communication skills, capable of articulating complex technical concepts to non-technical stakeholders.