Job description

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. This isn't a role with a predefined checklist; it's an opportunity to help define the "what" and the "how." 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. Responsibilities We are seeking exceptional individuals who will help to propose, design, and evaluate analog-computing hardware architectures for achieving a 1000x energy-efficiency advantage in ML inference. Minimum Qualifications Exceptional technical training and ability in a quantitative field (e.g., Physics, Electrical Engineering, Computer Science, or a related discipline): An MS/PhD or equivalent research/project experience is strongly preferred. Deep knowledge of and experience in at least one of: AI hardware architecture design or integrated-circuit design (the latter need not relate directly to AI). Analog and/or other unconventional physics-based computing : Exposure to and familiarity with the basic principles and engineering practice of these paradigms. Modern generative AI models : Familiarity with how modern (post-2017) AI models work and what this might imply for analog computer systems design. Full-stack Communication: Excellent ability to communicate complex technical concepts to diverse teams across the full stack from algorithms to circuits. Preferred Qualifications (Nice to Haves) The ideal candidate will have a deep understanding of the principles and engineering practice of analog and/or other unconventional physics-based computing paradigms and a proven ability to design and evaluate analog/unconventional computing hardware architectures . Energy modeling and optimization : Experience modeling the energy consumption of given architectural designs for AI workloads, and optimizing architectures for minimal energy consumption while maintaining ML performance. Hardware-algorithm co-design : Experience designing systems where both the AI algorithm/model and the hardware are optimized simultaneously - as opposed to architecting a system where the system behavior is specified a priori based on the needs of algorithms as they are run on GPUs. Modern generative AI models : Experience with designing hardware to run modern generative AI models. Non-volatile memories : Experience with detailed systems design and evaluation using at least one type of emerging non-volatile memory (e.g., RRAM), and knowledge of the tradeoffs of other non-volatile memories. Why Join Us? The Mission: Redefine computing for the next 50 years by solving the fundamental energy limitation of AI at a global scale. The Impact: Shape the company's future as a foundational team member. Enjoy massive ownership and an outsized opportunity to drive change. The Challenge: Dive into deeply complex, intellectually stimulating, and unsolved problems at the cutting edge of multiple, converging 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.

Requirements

Must-haves

As a Member of Technical Staff, you will be a foundational member of our small, multi-disciplinary R&D team. This isn't a role with a predefined checklist; it's an opportunity to help define the "what" and the "how."
We are seeking exceptional individuals who will help to propose, design, and evaluate analog-computing hardware architectures for achieving a 1000x energy-efficiency advantage in ML inference.
Analog and/or other unconventional physics-based computing : Exposure to and familiarity with the basic principles and engineering practice of these paradigms.
Modern generative AI models : Familiarity with how modern (post-2017) AI models work and what this might imply for analog computer systems design.
Full-stack Communication: Excellent ability to communicate complex technical concepts to diverse teams across the full stack from algorithms to circuits.
Energy modeling and optimization : Experience modeling the energy consumption of given architectural designs for AI workloads, and optimizing architectures for minimal energy consumption while maintaining ML performance.
Modern generative AI models : Experience with designing hardware to run modern generative AI models.
Non-volatile memories : Experience with detailed systems design and evaluation using at least one type of emerging non-volatile memory (e.g., RRAM), and knowledge of the tradeoffs of other non-volatile memories.
Why Join Us?
The Mission: Redefine computing for the next 50 years by solving the fundamental energy limitation of AI at a global scale.
The Impact: Shape the company's future as a foundational team member. Enjoy massive ownership and an outsized opportunity to drive change.
The Challenge: Dive into deeply complex, intellectually stimulating, and unsolved problems at the cutting edge of multiple, converging fields.

Nice-to-haves

Preferred Qualifications (Nice to Haves)

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aiml

Platform: greenhouse
Effort: quick

Benefits

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401k

Pto