AI Research Scientist

About Us:

webAI is pioneering the future of artificial intelligence by establishing the first distributed AI infrastructure dedicated to personalized AI. We recognize the evolving demands of a data-driven society for scalability and flexibility, and we firmly believe that the future of AI lies in distributed processing at the edge, bringing computation closer to the source of data generation. Our mission is to build a future where a company's valuable data and intellectual property remain entirely private, enabling the deployment of large-scale AI models directly on standard consumer hardware without compromising the information embedded within those models. We are developing an end-to-end platform that is secure, scalable, and fully under the control of our users, empowering enterprises with AI that understands their unique business. We are a team driven by truth, ownership, tenacity, and humility, and we seek individuals who resonate with these core values and are passionate about shaping the next generation of AI.

About the Role:

The AI Research Scientist will work within webAI’s Intelligence Lab as the “brain” of development. Their primary objective is to push the boundaries of what is possible. While they collaborate with engineers, their success is measured by the novelty and efficacy of their models, often operating in a high-uncertainty environment. This individual will work closely with research leadership and applied AI teams to advance scientific discovery while ensuring that innovations translate into real-world impact.

This is a hands-on research role for someone who loves experimentation, solving complex problems, and building AI that is powerful, efficient, and privacy-preserving. This position is based in-office at our downtown headquarters.

Responsibilities:

• Algorithm Development: Design and train novel architectures (e.g., Transformers, State-Space Models, Diffusion) to solve complex business problems.
• Experimental Design: Lead the hypothesis-testing phase, defining evaluation benchmarks and loss functions that align with human intent.
• Literature Leadership: Stay at the bleeding edge of ArXiv and academic conferences (NeurIPS, ICML, CVPR) to implement or improve upon the latest research.
• Prototyping: Build "proof-of-concept" models that demonstrate a significant leap in accuracy, speed, or reasoning capability.
• Knowledge Transfer: Document findings and mentor engineers on the mathematical nuances of the models.

Qualifications:

• Experience: 5+ years of post-doc or industrial research experience or equivalent university time with a deep experience that matches WIL objectives to include with a track record of publications or significant model releases.
• Technical Depth: Mastery of linear algebra, calculus, and probability; deep proficiency in Python and deep learning frameworks.
• Education: PhD in Machine Learning, Mathematics, Physics, or a highly quantitative field.