Job Description
Nexus Future Labs is pioneering the next generation of intelligent systems, with a bold goal to redefine the technological landscape by 2026. We are seeking a visionary Senior AI & Future Systems Architect to lead our engineering division in San Francisco. In this pivotal role, you will design the foundational architecture for our next-generation autonomous systems and generative AI models.
As we race toward our 2026 objectives, we need a leader who isn't just keeping up with trends but is setting them. You will be responsible for bridging the gap between theoretical AI research and scalable, production-ready software. If you are passionate about the future of computing and want to build systems that think, learn, and adapt, we want to hear from you.
Why Join Us?
We offer a competitive compensation package, equity opportunities, and the chance to work on projects that will shape the world of tomorrow.
Responsibilities
- Architect and implement scalable machine learning pipelines tailored for high-frequency trading and autonomous navigation systems.
- Lead the research and integration of emerging AI paradigms, including neuromorphic computing and quantum-ready algorithms.
- Define the technical roadmap for our 2026 product launch, ensuring feasibility and performance benchmarks.
- Optimize existing neural network models to reduce latency and improve inference speed on edge devices.
- Establish best practices for AI ethics, bias mitigation, and explainable AI (XAI) within the organization.
- Collaborate closely with cross-functional teams in Product, Design, and Engineering to deliver user-centric AI solutions.
- Oversee the migration of legacy infrastructure to modern, serverless AI architectures.
Qualifications
- Master’s degree or Ph.D. in Computer Science, Artificial Intelligence, or a related technical field.
- Minimum of 8+ years of professional experience in software engineering, with at least 5 years specifically in AI/ML architecture.
- Deep proficiency in programming languages such as Python, C++, and Rust.
- Extensive experience with deep learning frameworks (TensorFlow, PyTorch) and distributed computing systems (Kubernetes, Docker).
- Strong background in Natural Language Processing (NLP) and Large Language Models (LLMs).
- Proven track record of leading engineering teams and managing complex technical projects.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and MLOps tooling.