Asks for

The ad says“ML/AI Familiarity: Familiarity with dynamics-based ML model architectures, such as diffusion models and energy-based models, and general experience with ML model training flows. Experience with using high-level ML model frameworks, such as PyTorch and JAX.”
The ad says“ML/AI Familiarity: Familiarity with dynamics-based ML model architectures, such as diffusion models and energy-based models, and general experience with ML model training flows. Experience with using high-level ML model frameworks, such as PyTorch and JAX.”
The ad says“ML/AI Familiarity: Familiarity with dynamics-based ML model architectures, such as diffusion models and energy-based models, and general experience with ML model training flows. Experience with using high-level ML model frameworks, such as PyTorch and JAX.”
The ad says“Programming Proficiency: Strong command of Python and expertise with using numeric computing and visualization libraries, such as numpy, scipy, and matplotlib. Experience with libraries geared toward analysis, such as computer algebra libraries (e.g., sympy) also recommended.”
The ad says“Analytic Foundations: Core competences in the analysis of nonlinear dynamical systems (ODEs, PDEs, SDEs), ideally with experience analyzing the stability, noise robustness, and capacity of such systems. Strong candidates are able to leverage the properties of (or impose constraints on) such systems to develop analytic insights.”
The ad says“Programming Proficiency: Strong command of Python and expertise with using numeric computing and visualization libraries, such as numpy, scipy, and matplotlib. Experience with libraries geared toward analysis, such as computer algebra libraries (e.g., sympy) also recommended.”
The ad says“Analytic Foundations: Core competences in the analysis of nonlinear dynamical systems (ODEs, PDEs, SDEs), ideally with experience analyzing the stability, noise robustness, and capacity of such systems. Strong candidates are able to leverage the properties of (or impose constraints on) such systems to develop analytic insights.”
The ad says“Analytic Foundations: Core competences in the analysis of nonlinear dynamical systems (ODEs, PDEs, SDEs), ideally with experience analyzing the stability, noise robustness, and capacity of such systems. Strong candidates are able to leverage the properties of (or impose constraints on) such systems to develop analytic insights.”
The ad says“Programming Proficiency: Strong command of Python and expertise with using numeric computing and visualization libraries, such as numpy, scipy, and matplotlib. Experience with libraries geared toward analysis, such as computer algebra libraries (e.g., sympy) also recommended.”
The ad says“ML/AI Familiarity: Familiarity with dynamics-based ML model architectures, such as diffusion models and energy-based models, and general experience with ML model training flows. Experience with using high-level ML model frameworks, such as PyTorch and JAX.”
The ad says“Programming Proficiency: Strong command of Python and expertise with using numeric computing and visualization libraries, such as numpy, scipy, and matplotlib. Experience with libraries geared toward analysis, such as computer algebra libraries (e.g., sympy) also recommended.”
The ad says“Analytic Foundations: Core competences in the analysis of nonlinear dynamical systems (ODEs, PDEs, SDEs), ideally with experience analyzing the stability, noise robustness, and capacity of such systems. Strong candidates are able to leverage the properties of (or impose constraints on) such systems to develop analytic insights.”

About Unconventional

Since 2022, AI has entered the mainstream, reshaping entire industries from education and software development to fundamental consumer behaviors. This revolution has created an unprecedented demand for computation - a demand that is now fundamentally limited by energy, not just in the datacenter, but at a global scale.

At Unconventional, our mission is to solve this. We are rethinking computing from the ground up to build a new foundation for AI that is 1000x more efficient. We're doing this by exploiting the rich physics of semiconductors, mapping neural networks directly to the device physics rather than relying on layers of inefficient abstraction.

The Role

As a Member of Technical Staff, you will be a foundational member of our small, multi-disciplinary R&D team. We are looking for 'first principles' thinkers who are excited to tackle the hardest, most ambiguous technical challenges at the intersection of AI, physics, and computer architecture. You will be responsible for driving invention, prototyping, and validation of the core components of our novel computing platform.

Your work will be fluid and could span from theoretical modeling and simulation to algorithm development, hardware/software co-design, or experimental validation in collaboration with other team members. We're hiring exceptional problem-solvers who can navigate deep uncertainty and help chart our technical roadmap.

What We're Looking For

Exceptional technical ability in a quantitative field (e.g., Physics, Computer Science, Electrical Engineering, Applied Math, or a related discipline). An MS/PhD or equivalent research/project experience is strongly preferred.
A "0-to-1" mindset. You have a demonstrated history of tackling complex, ambiguous R&D problems, often from a blank slate.
Deep curiosity. You are comfortable diving into new domains, whether it's semiconductor physics, machine learning theory, or systems-level design.
A creative and unconventional approach to problem-solving.

Core Technical Competencies

Analytic Foundations: Core competences in the analysis of nonlinear dynamical systems (ODEs, PDEs, SDEs), ideally with experience analyzing the stability, noise robustness, and capacity of such systems. Strong candidates are able to leverage the properties of (or impose constraints on) such systems to develop analytic insights.
Practical Eye: The ability to leverage analytic insights to build practical tools – metrics, algorithmic optimizations, and automated analyses – that can be used to study dynamical systems. Strong candidates are comfortable navigating the tradeoff space between theoretic purity and practicality to realize useful tooling.
Programming Proficiency: Strong command of Python and expertise with using numeric computing and visualization libraries, such as numpy, scipy, and matplotlib. Experience with libraries geared toward analysis, such as computer algebra libraries (e.g., sympy) also recommended.
ML/AI Familiarity: Familiarity with dynamics-based ML model architectures, such as diffusion models and energy-based models, and general experience with ML model training flows. Experience with using high-level ML model frameworks, such as PyTorch and JAX.

Bonus Points (Nice to Have)

Compute model technical staff may focus primarily on the above skillsets, or may be cross-disciplinary with hardware or AI/ML algorithms expertise.
Collaboration & Communication
- Cross-Functional Leadership: Excellent ability to translate complex technical concepts for diverse teams. You will act as a translator, discussing algorithmic/model trade-offs with ML/AI teams and eliciting hardware constraints and features from hardware engineering teams.

Why Join Us?

The Mission: Tackle a fundamental problem that could redefine computing for the next 50 years.
The Impact: Be a foundational member of a world-class team with an outsized opportunity for ownership and impact.
The Challenge: Work on deeply challenging, intellectually stimulating problems that sit at the cutting edge of multiple fields.
The Perks: A comprehensive package including best-in-class health benefits, 401k matching, truly unlimited PTO, and complimentary meals in our Palo Alto office.