Research Scientist - AI

We are seeking a Research Scientist to drive innovation in advanced information processing and explore diverse AI applications. This role focuses on the intersection of algorithm development, complex problem formulation, and experimental validation.

The successful candidate will excel at translating physical or hardware-constrained challenges into solvable computational models. While initial work focuses on high-precision information processing (e.g., signal recovery and denoising), this position emphasizes strong AI fundamentals and the ability to apply technical reasoning to a wide range of emerging problem domains.

• Algorithm Design: Implement and evaluate algorithms for signal and information processing, focusing on high-fidelity recovery from low signal-to-noise ratio (SNR) datasets.
• Modeling: Analyze data characteristics, including noise modeling, feature extraction, and identifying structured patterns in high-dimensional data.
• Optimization: Formulate complex problems using objective functions, regularization techniques, and optimization-based approaches.
• Benchmarking: Rigorously test novel methods against classical baselines and state-of-the-art AI architectures using quantitative performance metrics.

• Prototyping: Develop proof-of-concept AI applications for emerging fields involving hardware-software co-design and physics-informed neural networks.
• Feasibility Studies: Perform technical deep-dives into new problem sets to determine the viability of AI-driven solutions.
• Architectural Analysis: Compare alternative modeling approaches and provide data-driven recommendations to the leadership team.
• Problem Definition: Work with technical leads to refine research questions and translate business needs into experimental designs.
• Technical Documentation: Maintain clear records of assumptions, methodologies, and results for internal technical notes and potential publications.
• Visualization: Produce high-quality data visualizations to communicate complex comparative analyses to stakeholders.

• Education: PhD (preferred) or Master’s degree with 3+ years of relevant experience in Applied Mathematics, Physics, Electrical Engineering, Computer Science, or a related quantitative field.
• Versatile AI Portfolio: Demonstrated experience across varied AI applications, specifically showing the ability to map different architectures to unique mathematical or computational constraints.
• Core Technical
  
  Competencies:
  
  
• Mathematical Foundations: Solid understanding of optimization, objective-function design, or energy-based modeling.
• Signal Processing: Strong background in general signal/information processing or computational imaging.
• Programming: Proficiency in Python and the scientific ML stack (Num Py, Sci Py, PyTorch, JAX, or equivalent).