Job Description
Are you ready to shape the technology of 2026 and beyond?
Nexus Future Labs is at the forefront of Artificial Intelligence, pioneering next-generation machine learning models that redefine human-computer interaction. We are seeking a visionary Senior AI Engineer to join our elite technical team in San Francisco. In this role, you will architect scalable machine learning systems, optimize deep learning algorithms, and lead the development of AI solutions that solve complex, real-world problems.
Join us in building the future of intelligent systems and work with cutting-edge technologies including Large Language Models (LLMs), Computer Vision, and Neural Networks.
Responsibilities
- Model Development & Training: Design, train, and fine-tune advanced AI models, including LLMs and Transformers, to achieve state-of-the-art performance.
- System Architecture: Build and optimize scalable MLOps pipelines for model deployment, monitoring, and retraining.
- Data Strategy: Lead the strategy for data ingestion, processing, and curation to ensure high-quality training datasets.
- Code Quality: Write clean, efficient, and maintainable Python code; conduct rigorous code reviews to ensure engineering best practices.
- Collaboration: Partner with cross-functional teams of data scientists, product managers, and engineers to integrate AI solutions into production environments.
- Innovation: Stay abreast of the latest research in AI/ML and experiment with emerging technologies to drive innovation within the organization.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related field with a focus on Artificial Intelligence.
- Experience: 5+ years of professional experience in AI/ML engineering, with a proven track record of deploying production-ready models.
- Technical Skills: Expert proficiency in Python (PyTorch, TensorFlow, or JAX) and C++.
- Machine Learning: Deep understanding of classical and modern machine learning algorithms (e.g., CNNs, RNNs, GANs) and deep learning architectures.
- MLOps: Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- Problem Solving: Strong analytical skills with the ability to troubleshoot complex technical challenges and optimize system performance.