Job Description
We are seeking a visionary Senior AI Research Scientist to lead our initiatives towards the 2026 Vision. In a rapidly evolving technological landscape, you will be instrumental in defining the next generation of autonomous agents and multimodal AI systems. Join a team that pushes the boundaries of possibility, ensuring our solutions remain ahead of the curve.
Why Join Us?
We are not just building software; we are architecting the intelligence of tomorrow. You will work in a state-of-the-art facility with access to the latest hardware, competitive equity packages, and a culture that rewards innovation and bold thinking.
Responsibilities
- Architect Next-Gen Models: Design and implement scalable AI architectures focused on AGI readiness and autonomous decision-making capabilities for the 2026 timeframe.
- Optimize Performance: Improve model efficiency, reduce inference costs, and enhance the accuracy of Large Language Models (LLMs) and multimodal systems.
- R&D Leadership: Spearhead research projects exploring novel neural network structures and reinforcement learning techniques to solve complex problems.
- Technical Mentorship: Guide a team of junior data scientists and engineers, fostering a culture of continuous learning and technical excellence.
- Cross-Functional Collaboration: Partner with product management and engineering teams to translate theoretical research into practical, deployable applications.
- Publication & Thought Leadership: Contribute to top-tier academic journals and present at major industry conferences to establish our brand as a pioneer in AI.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Mathematics, Physics, or a related field.
- Experience: Minimum of 5+ years of professional experience in AI/ML research, specifically within deep learning or natural language processing.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and experience with distributed training frameworks.
- Domain Knowledge: Deep understanding of transformer architectures, attention mechanisms, and fine-tuning strategies (RLHF, LoRA).
- Problem Solving: Proven track record of solving unsolved problems in machine learning and a strong mathematical background in linear algebra and probability.
- Communication: Excellent written and verbal communication skills with the ability to explain complex technical concepts to diverse audiences.