Summary
The job is an AI Research Residency at Normal Computing, lasting 6-12 months with the possibility of extension. The role involves developing cutting-edge AI algorithms and hardware paradigms for a physics-based AI platform. The position can be based in New York or remote.
Requirements
- Experience with large-scale numerical simulations, including benchmarking of ML algorithms and training of ML models
- Experience with modern AI methods, such as probabilistic ML, Bayesian reasoning, sampling algorithms, and generative AI models
- Familiarity with classical physics formalism, differential equations, and stochastic processes
- Familiarity with characterizing the impact of noise and imperfections on algorithmic performance
- Familiarity with data science applications and specific use cases of ML methods
- Proficiency in at least one programming language, with a preference for those commonly used in ML or scientific computing such as Python or C++
- Familiarity with TensorFlow, PyTorch, Jax, NumPy, Pandas, or similar ML/scientific libraries
Responsibilities
- Interface with our world-class research team focused on developing a full-stack physics-based AI platform
- Explore cutting-edge generative AI tools for novel applications
- Research and develop new algorithms and hardware paradigms for AI
- Conduct numerical benchmarking of algorithmic and hardware proposals
- Optimize hardware speedups over state-of-the-art and characterize the impact of hardware noise
- Investigate commercial applications that stand to benefit from Normalβs physics-based AI platform
Benefits
- Competitive salary
- Benefits including medical (Depending on location)
- Opportunity to explore a full-time role at Normal Computing after the residency