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About the Role

Develop computational methods for efficient AI inference on Normal's thermodynamic hardware, rethinking operations for stochastic analog computation in memory. This co-design role involves influencing architectural decisions by understanding transformer and diffusion workloads for stochastic analog execution and designing numerical methods that map onto the hardware's physical dynamics.

Responsibilities

• Develop algorithms for transformer inference workloads running on stochastic analog processing-with-memory hardware.
• Work directly with hardware and architecture teams to shape what the chip can and should compute natively.
• Design numerical methods that exploit thermal noise and analog dynamics.
• Build evaluation frameworks and benchmarks to characterize algorithm behavior on real hardware or simulation.
• Translate insights about model workloads into constraints and opportunities for hardware design.
• Prototype and iterate rapidly as hardware evolves from simulation to silicon.

Requirements

• Deep understanding of large model inference, including attention mechanisms, KV cache, long-context decoding, and memory bandwidth constraints.
• Experience with inference optimization techniques such as 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.
• 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.

Skills

• Python
• Systems programming languages
• Algorithm development
• Software/hardware co-design
• Numerical methods
• Evaluation and benchmarking
• Workload translation
• Rapid prototyping

Location

• New York
• Silicon Valley
• London
• Copenhagen
• Seoul

Work Type

• Full-time

Experience Level

• All seniority levels

Education Level

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

About the Company

• Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource, resulting in 10-100x more AI inference per dollar, per watt.
• They co-design the full stack: AI-native EDA systems and advanced ASICs.
• Backed by over $85M in funding from leading deep-tech investors.
• Built by scientists, engineers, and operators from pioneering technology labs.
• Operates as one team across New York, Silicon Valley, London, Copenhagen, and Seoul.

Equal Opportunity

• 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.
• Normal Computing is committed to providing reasonable accommodations to individuals with disabilities.
• 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 Privacy Policy.