Job Description
Shape the Future of Intelligence
Quantum Nexus Labs is at the forefront of the 2026 AI revolution. We are building the neural infrastructure that will power the next generation of autonomous systems and generative workflows. We are seeking a visionary Senior AI Research Engineer to join our elite team in San Francisco.
In this role, you won't just be maintaining models; you will be architecting the future. You will work on cutting-edge Large Language Models (LLMs), reinforcement learning agents, and multimodal AI systems designed to solve problems that currently seem impossible.
What You Will Do:
You will drive the technical vision for our research initiatives, ensuring our solutions are scalable, ethical, and future-proof for the 2026 market.
Responsibilities
- Lead the research and development of next-generation AI models, specifically focusing on Generative AI and Autonomous Agents.
- Design and implement novel neural network architectures to improve model performance, accuracy, and efficiency.
- Optimize inference pipelines and reduce latency for real-time applications in high-stakes environments.
- Collaborate with product and engineering teams to integrate complex AI research into production-ready software.
- Mentor a team of junior data scientists and engineers, fostering a culture of technical excellence and innovation.
- Stay ahead of global AI trends to ensure Quantum Nexus remains a leader in the 2026 landscape.
- Conduct rigorous testing and validation of AI models to ensure robustness and security.
Qualifications
- PhD or Master’s degree in Computer Science, Statistics, Mathematics, or a related field with a focus on AI/ML.
- Minimum of 6 years of experience in Machine Learning research and engineering.
- Extensive experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Strong proficiency in programming languages, primarily Python, with experience in C++ for performance optimization.
- Deep understanding of transformer models, attention mechanisms, and fine-tuning strategies.
- Experience with MLOps tools (Docker, Kubernetes, MLflow) and cloud infrastructure (AWS/GCP).
- Proven track record of publishing research in top-tier conferences or delivering production-grade AI systems.