Job Description
Are you ready to define the trajectory of Artificial Intelligence in 2026?
Nexus AI Labs is seeking a visionary Senior AI Research Scientist to lead our next-generation Generative AI initiatives. As we race toward the 2026 technological horizon, you will be at the forefront of developing autonomous agents, multimodal models, and next-level reasoning systems.
We are looking for a thought leader who doesn't just implement existing solutions but architects the future of intelligent systems. If you are passionate about pushing the boundaries of Large Language Models (LLMs) and building safe, scalable AI infrastructure, we want to hear from you.
Why Join Us?
- Work on cutting-edge projects that define the 2026 AI landscape.
- Competitive compensation package and equity opportunities.
- Collaborate with world-class engineers and researchers.
- Flexible remote and hybrid work options.
Responsibilities
- Lead Research & Development: Spearhead the research and implementation of advanced Generative AI models, focusing on LLMs, Diffusion models, and Reinforcement Learning from Human Feedback (RLHF).
- Model Optimization: Architect efficient training pipelines and optimize model inference for production-scale deployment, ensuring low latency and high throughput.
- Paper Publication: Publish high-impact research papers in top-tier conferences (NeurIPS, ICML, ICLR) to establish Nexus AI Labs as a thought leader in the AI community.
- Cross-Functional Collaboration: Partner with product teams to translate complex research concepts into practical, user-facing AI applications.
- Code Review & Mentorship: Mentor junior researchers and engineers, maintaining high standards of code quality and research rigor across the organization.
- Ethical AI: Implement safety guardrails and ethical guidelines to ensure AI outputs are unbiased, transparent, and aligned with human values.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Physics, or a related technical field with a focus on AI/ML.
- Experience: 5+ years of professional experience in AI/ML research or software engineering, specifically within deep learning frameworks.
- Technical Stack: Deep proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with distributed training systems (Ray, Kubernetes) is highly preferred.
- Domain Knowledge: Strong understanding of Natural Language Processing (NLP), Computer Vision, or Multi-modal learning architectures.
- Research Skills: Proven ability to design novel architectures and experiment with new techniques to solve complex problems.
- Communication: Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to diverse stakeholders.