Uare.ai is an AI startup aiming to empower people to do more with their memories by creating AI-driven personal digital twins. The company needs to lead data science initiatives and drive AI research excellence to build its platform and translate cutting-edge research into production systems.
Requirements
- Deep Mathematical Foundation: Expert knowledge in statistics, linear algebra, calculus, optimization theory, and probability theory.
- Theoretical & Applied Expertise: Strong background in both theoretical machine learning and practical applications in industry settings.
- Modern LLM and ML Stack Proficiency: Expert-level experience with current LLM and ML frameworks and tools relevant for Q2 2025.
- Programming Excellence: Advanced Python skills with experience in C++, Julia, or Rust for performance-critical applications.
- Statistical Modeling: Expertise in optimization, numerical methods, Bayesian methods, time series analysis, causal inference, and experimental design.
- Research Leadership: Experience leading research projects from conception to publication and production deployment.
- DevOps experience: CI/CD pipelines, Infrastructure-as-code, SysAdmin skills across Linux
Responsibilities
- Drive advanced research initiatives in machine learning, deep learning, and statistical modeling with direct applications to personal AI systems.
- Develop mathematical frameworks and statistical models to solve tough problems in AI personalization and digital twin technologies.
- You understand how to architect multi-layered data systems using cutting edge (2025) technologies.
- Conduct rigorous statistical analysis, hypothesis testing, and causal inference to validate AI model performance and business impact.
- Develop cutting-edge analytics capabilities using the latest ML software stacks and research methodologies
- As a sophisticated scientist, you should be comfortable with experimental design and look forward to testing your hypotheses.
Other
- PhD in Mathematics, Statistics, Computer Science, Physics, or related quantitative field
- 8+ years of combined academic research and industry experience in data science, machine learning, or AI research.
- Postdoctoral research experience or equivalent advanced research roles.
- Background in personalization systems, recommendation engines, or conversational AI.
- Active contributions to major ML/AI open source projects
