Job Description
Are you ready to define the trajectory of Artificial Intelligence in 2026 and beyond?
Nexus Horizon Labs is seeking a visionary Senior AI Research Scientist to lead our next-generation foundation model initiatives. We are not just building software; we are architecting the future of human-computer interaction. Join a world-class team dedicated to solving the hardest problems in generative AI, autonomous agents, and scalable machine learning systems.
In this pivotal role, you will bridge the gap between theoretical breakthroughs and practical, high-impact applications. If you are passionate about pushing the boundaries of what is possible in AI and want to leave a lasting legacy in the tech industry, we want to hear from you.
Responsibilities
- Lead R&D: Spearhead the research and development of state-of-the-art deep learning architectures and algorithms focused on large language models and multimodal systems.
- Innovation: Design novel neural network structures and training methodologies to improve model efficiency, accuracy, and generalization capabilities.
- Productionization: Translate cutting-edge research into scalable, production-ready code and collaborate closely with the engineering team to deploy robust AI systems.
- Mentorship: Guide and mentor junior researchers and data scientists, fostering a culture of technical excellence and continuous learning.
- Publication: Author high-impact research papers and present findings at top-tier international conferences (NeurIPS, ICML, ICLR).
- Strategy: Contribute to the long-term technical vision and strategy of the company's AI roadmap.
Qualifications
- Education: PhD or Master's degree in Computer Science, Mathematics, Statistics, or a related quantitative field with a focus on AI/ML.
- Experience: 5+ years of proven experience in research, development, and deployment of machine learning models.
- Technical Skills: Deep expertise in Python, PyTorch, TensorFlow, or JAX. Strong understanding of distributed training, optimization, and system design.
- Domain Knowledge: Solid background in Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems and derive innovative solutions.
- Communication: Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to diverse audiences.