Job Description
Are you ready to define the future of technology in 2026? Nexus Future Labs is seeking a visionary Senior AI Research Scientist to spearhead our next-generation machine learning initiatives. We are building the infrastructure for tomorrow, and we need a pioneer to lead our research in generative AI and large-scale neural networks.
As a Senior AI Research Scientist at Nexus, you won't just implement existing models; you will architect the algorithms that will power our ecosystem through the next decade. We offer a highly competitive compensation package, equity packages, and the opportunity to work on cutting-edge problems that shape the industry.
Why Join Us?
- Impactful Work: Directly influence the roadmap of our flagship products launching in 2026.
- Top-Tier Talent: Collaborate with world-class engineers and researchers.
- Flexible Environment: Hybrid work model with a focus on autonomy and innovation.
Responsibilities
- Research Leadership: Spearhead R&D in Large Language Models (LLMs), Computer Vision, and Reinforcement Learning to achieve state-of-the-art performance metrics.
- Architecture Design: Design scalable, robust, and efficient neural network architectures that can handle billions of parameters.
- Production Deployment: Bridge the gap between theoretical research and production-grade code, optimizing models for latency and throughput.
- Publication & Mentoring: Publish high-impact research papers at top-tier conferences (NeurIPS, ICML, ICLR) and mentor junior researchers and data scientists.
- Collaboration: Work closely with product managers and engineering teams to translate research goals into tangible business solutions.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Artificial Intelligence, Mathematics, or a related field.
- Experience: 5+ years of proven experience in AI/ML research and development within a high-tech environment.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, and CUDA. Experience with distributed computing frameworks (Ray, Kubernetes) is highly desirable.
- Domain Knowledge: Deep understanding of deep learning principles, optimization techniques, and natural language processing.
- Communication: Excellent ability to communicate complex technical concepts to non-technical stakeholders and research findings to the scientific community.