Job description

NORMAL COMPUTING | BUILD WITH US 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 world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul. THE ROLE Normal's hardware runs the most memory-bound parts of large-model inference inside memory itself, rather than shuttling weights to a processor. The software that makes this programmable doesn't exist yet in any standard form. You will develop the computational methods that make LLM inference run efficiently on it. The core challenge is rethinking how operations like attention, memory access, and long-context decoding should behave on hardware that computes inside memory, rather than adapting standard GPU kernels to a new chip. Your job is to develop the algorithms that exploit this natively: understand which parts of LLM workloads are best suited to in-memory execution, design numerical methods that map onto the hardware's physical behavior, and validate them against our simulators and FPGA prototypes first, and against silicon as it arrives. This is a co-design role. The hardware and the algorithms are developed in parallel, which means you will influence architectural decisions rather than implement against a fixed specification. The strongest candidates have a deep understanding of both large model inference and the mathematics of stochastic systems, and have built systems that run on real hardware. WHAT YOU WILL OWN - Algorithm Development: Develop algorithms for LLM inference workloads running on our in-memory hardware. Success means algorithms that measurably improve LLM inference on our hardware, demonstrated in simulation and then on silicon. - Software/Hardware Co-Design: Work directly with hardware and architecture teams to shape what the chip can and should compute natively. - Numerical Methods: Design numerical methods that exploit thermal noise and analog dynamics rather than working around them. - Evaluation & Benchmarks: Build evaluation frameworks and benchmarks that characterize algorithm behavior on real hardware or simulation. - Workload Translation: Translate insights about model workloads into constraints and opportunities for hardware design. - Rapid Prototyping: Prototype and iterate rapidly as hardware evolves from simulation to silicon. - Performance Optimization: Optimize performance at both the gate level and the algorithmic level. WHAT MAKES YOU A GREAT FIT - Deep understanding of large model inference: attention mechanisms, KV cache, long-context decoding, memory bandwidth constraints - Experience with inference optimization: quantization, sparsity, kernel fusion, or memory-efficient attention - Familiarity with stochastic systems, probabilistic methods, numerical analysis, or analog computation - Experience implementing algorithms close to hardware, beyond high-level frameworks - Comfort reasoning from first principles about what a novel substrate can do efficiently - Track record of taking ideas from theory to working implementation on real hardware - Strong programming skills in Python and at least one systems language - Collaborative instinct and ability to work across hardware, architecture, and software teams BONUS POINTS - PhD in machine learning, applied mathematics, physics, electrical engineering, or a related field - Exposure to analog or mixed-signal systems, in-memory compute, or non-von-Neumann architectures - Experience working on hardware that did not yet exist when you joined - Publications or open-source work in efficient inference, stochastic algorithms, or novel 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 accommodations@normalcomputing.com. 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 Applicant Privacy Notice normalcomputing.com.

Requirements

Must-haves

Software/Hardware Co-Design: Work directly with hardware and architecture teams to shape what the chip can and should compute natively.
Numerical Methods: Design numerical methods that exploit thermal noise and analog dynamics rather than working around them.
Evaluation & Benchmarks: Build evaluation frameworks and benchmarks that characterize algorithm behavior on real hardware or simulation.
Workload Translation: Translate insights about model workloads into constraints and opportunities for hardware design.
Rapid Prototyping: Prototype and iterate rapidly as hardware evolves from simulation to silicon.
Performance Optimization: Optimize performance at both the gate level and the algorithmic level.
Deep understanding of large model inference: attention mechanisms, KV cache, long-context decoding, memory bandwidth constraints
Experience with inference optimization: quantization, sparsity, kernel fusion, or memory-efficient attention
Familiarity with stochastic systems, probabilistic methods, numerical analysis, or analog computation
Experience implementing algorithms close to hardware, beyond high-level frameworks
Comfort reasoning from first principles about what a novel substrate can do efficiently
Track record of taking ideas from theory to working implementation on real hardware
Strong programming skills in Python and at least one systems language
Collaborative instinct and ability to work across hardware, architecture, and software teams

Nice-to-haves

PhD in machine learning, applied mathematics, physics, electrical engineering, or a related field
Exposure to analog or mixed-signal systems, in-memory compute, or non-von-Neumann architectures
Experience working on hardware that did not yet exist when you joined
Publications or open-source work in efficient inference, stochastic algorithms, or novel computing

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmpythonmachine learningaccessibility

Platform: ashby
Effort: quick