Looking to advance your career in AI? The AI Research Residency program presents an exclusive 6-12 month opportunity to accelerate your career in modern AI research. As an AI Research Resident at Normal Computing, you’ll have the incredible opportunity to collaborate with our world-class research team in bringing to life a full-stack physics-based AI platform. Your role will involve developing cutting-edge algorithms and hardware paradigms for AI, pushing the boundaries of what’s possible.
If you’re ready to be on the frontier of AI research and share our vision for the synergy between physics and AI, we invite you to apply to our AI Residency Program and be part of the exciting future of AI at Normal Computing.
Your Role in Our Mission as an AI Research Resident:
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 Normal’s physics-based AI platform.
What Makes You A Great Fit:
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.
Residency FAQs:
Is this a part-time or full-time program?
Our residency is a full-time position lasting in duration between 6-12 months.
Can I be enrolled as a student at a university or work for another employer during the residency?
No, the residency can’t be completed simultaneously with any other obligations.
I have been out of school for several years. Am I eligible to apply?
Yes. We will consider applications from various backgrounds.
Is this a paid residency?
Yes. Residents are paid a competitive salary.
Will I receive benefits during the Residency?
Yes, residents are eligible for most benefits, including medical (Depending on location).
Will I be required to relocate for this residency?
Absolutely not. Residents are encouraged to work on-site at our New York office, however we are a distributed team and you are welcome to work from wherever you are currently located.
What options will be open to me at the end of the program?
While there is no guaranteed conversion to full-time employment, depending on the success of your residency, it would be our hope that there could be an opportunity to explore a full-time role at Normal Computing.
Equal Employment Opportunity Statement
Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.
Accessibility Accommodations
Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accomodations@normalcomputing.ai.
Privacy Notice
By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Privacy Policy.
