Apply to Normal ComputingAI Research Residency
Location: New York, NY (Hybrid or Remote)
Duration: 6-12 Months with the possibility of extension
Compensation: Competitive salary with benefits
About The Residency:
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.
Specifically, you will:
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.
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.
The experience we’re looking for:
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.
Additional Information:
Normal Computing is an equal opportunity employer. We are committed to building a diverse and inclusive workforce and do not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics, Normal Computing is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email accomodations@normalcomputing.ai
Normal is a New York-based deep-tech startup founded by former engineers from Google Brain, Alphabet X, and Palantir. Our investors include Celesta Capital, First Spark Ventures, Micron and former Google CEO Eric Schmidt. We engage with enterprise companies across various industries, including services, manufacturing, and the public sector, to deliver cutting-edge AI solutions.
We are on a mission to make AI universally scalable and useful.
Our products serve as critical full-stack infrastructure for our enterprise users in deploying AI into high-stakes applications. We are addressing the challenges of reliability, adaptivity, and auditability, which have traditionally been central barriers to adoption.
We believe that the untapped potential of AI to create transformative value remains to be fully realized. Thus far, AI has been subject to technological limitations such as unpredictable factual errors in generative AI (known as hallucinations) or lack of auditability as black-box models. Consequently, these limitations restrict the application of AI primarily to consumer-grade, low-stakes generative AI workflows and basic pattern recognition systems.
We envision that true transformative value can be unlocked in enterprise-grade, high-stakes AI workflows, where AI can reason reliably and autonomously, and understand its own limits. In these contexts, AI has the capacity to drive meaningful outcomes with real, complex impact.
Our approach involves redesigning AI systems from the ground up, contrasting other surface-level approaches. Our AI application development platform is powered by novel full-stack probabilistic machine learning infrastructure driven by thermodynamic physics. With Normal's probabilistic AI, we offer unprecedented control over reliability, adaptivity, and auditability to AI models, specifically tailored for critical and customer-specific enterprise workflows.
As we forge ahead with our mission, we are seeking passionate individuals eager to collaborate with our uniquely diverse and interdisciplinary team, and motivated by a workplace where the hardest problems remain to be solved.