Job Description
We are looking for a visionary Next-Gen AI Architect to lead our research into the technologies that will define 2026 and beyond. At Nexus Future Labs, we aren't just building software; we are architecting the future of human-machine interaction. If you possess a deep understanding of artificial intelligence, neural networks, and scalable system design, we want to hear from you.
In this pivotal role, you will bridge the gap between theoretical machine learning breakthroughs and production-ready systems. You will be responsible for designing the infrastructure that powers our next-generation generative AI models and autonomous agents. Join a team of elite engineers and scientists committed to pushing the boundaries of what is possible.
Why Join Us?
- Work on cutting-edge AI research that impacts millions.
- Competitive equity package and top-tier compensation.
- Flexible remote-first culture with state-of-the-art equipment.
- Focus on high-impact projects with long-term vision.
Responsibilities
- Design and architect scalable, high-performance AI systems capable of handling petabyte-scale data processing.
- Lead the research and implementation of novel neural network architectures, including Transformers and Graph Neural Networks.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to integrate AI capabilities into consumer products.
- Define technical roadmaps for AI infrastructure, ensuring alignment with long-term strategic goals.
- Mentor junior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Optimize model inference latency and improve energy efficiency in AI workloads.
Qualifications
- Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related technical field from a top-tier institution.
- Minimum of 5+ years of experience in machine learning engineering, deep learning, or AI architecture.
- Proficiency in programming languages such as Python, C++, or CUDA.
- Extensive experience with deep learning frameworks (TensorFlow, PyTorch, JAX) and MLOps tools (Kubeflow, MLflow).
- Strong understanding of distributed computing systems, cloud architecture (AWS, GCP, or Azure), and containerization (Docker, Kubernetes).
- Proven track record of deploying AI models to production environments.