Thermodynamic Hardware Residency

Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry critical AI infrastructure and the broader systems that power our world in partnership with the worlds most advanced institutions. We work as one team across New York City San Francisco London Copenhagen and Pangyo.

The Thermodynamic Hardware Residency is Normal Computings flagship program for exceptional researchers and engineers who want to work at the frontier of unconventional computing. Residents join a small hand-picked cohort with a dedicated research mentor direct access to the team building our cutting-edge thermodynamic hardware and a clear arc from onboarding through publication.

Every residency is built around two milestones that mark you as part of something bigger than a single project: a research paper co-authored with our team and a company-wide research colloquium where you present your findings to the full Normal Computing organization. Youll leave with a body of published presented work and a standing as one of the earliest residents to help define what this program becomes. Exceptional performers will be considered for full-time conversion at the end of the residency.

• Youll spend your residency embedded with the team building and characterizing our unconventional thermodynamic computing hardware silicon that exploits physical noise and analog dynamics rather than fighting them.

• This is a hands-on research residency: youll take ownership of a real technical problem at the boundary of physics hardware and machine learning work alongside the researchers and engineers building our hardware and be expected to contribute ideas not just execute someone elses.

• Our hardware is designed to deliver orders-of-magnitude more AI inference per dollar per watt than conventional GPUs not by porting existing GPU kernels onto new chips but by rethinking how core operations work when the substrate itself is stochastic analog computation in memory rather than conventional digital logic. That rethinking from device physics up through algorithms is exactly the kind of problem residents take on.

• Youll get direct exposure to the pace ambiguity and speed of decision-making that comes with working at a fast-moving well-funded hardware startup where the distance between an idea on a whiteboard and a test on real silicon is measured in weeks not years.

• Work hands-on with thermodynamic hardware. Help design simulate characterize or evaluate our hardware where noise analog dynamics and in-memory computation are first-class design elements rather than sources of error to be engineered away.

• Design numerical methods for a new substrate. Explore algorithms and numerical methods that exploit thermal noise and analog dynamics directly rather than adapting techniques built for conventional digital hardware.

• Drive a research question of your own. Partner with researchers and hardware engineers to scope run and iterate on an original technical investigation from device- or circuit-level physics up to algorithms and workloads that map onto thermodynamic compute.

• Build evaluation frameworks and benchmarks. Help build the tests and benchmarks that measure how algorithmic ideas actually perform on real hardware and in simulation and feed what you learn about model workloads back into hardware design decisions.

• Co-author a paper. Work with the team to write up your findings for submission to a relevant venue with mentorship on framing experiments and technical writing along the way one of the two milestones every resident builds toward.

• Present at the residency colloquium. Share your work and thinking with the broader Normal Computing research community at the programs capstone event and get real-time feedback from people building this technology every day.

• Experience startup pace firsthand. Work directly with founders senior researchers and engineers in a lean fast-moving environment where priorities shift quickly and your work has an outsized visible impact.

• Currently pursuing or recently completed a graduate degree (MS or PhD or equivalent research experience) in physics electrical engineering computer engineering computer science applied math or a related field with a focus on hardware device physics stochastic or analog computing or machine learning systems.

• Comfortable moving between levels of abstraction: from the physics of noise and analog devices to circuit- and architecture-level tradeoffs to the algorithms and workloads that will eventually run on this hardware.

• Some exposure to large-model inference concepts attention mechanisms KV caching long-context decoding and an interest in how they change when the underlying hardware isnt a GPU. Production-level experience isnt expected at the resident level but the intuition should feel familiar.

• Strong Python skills plus comfort with (or eagerness to learn) a lower-level systems language such as C or Rust; hands-on experience with simulation experimentation or hardware characterization (e.g. SPICE PyTorch FPGA or ASIC tooling) is a plus.

• First-principles reasoning about novel computational substrates: a genuine curiosity about unconventional computing where exploiting thermal noise rather than suppressing it sounds more interesting than intimidating.

• Strong written and verbal communication skills; prior experience writing up research (papers theses technical reports) is a plus as is any experience presenting technical work to a live audience.

• A bias toward ownership and self-direction: youre energized not overwhelmed by the ambiguity and speed of a small fast-moving startup.

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