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About the Role

The AI Research Residency is Normal Computing's flagship program for exceptional researchers and engineers who want to work at the frontier of AI. Residents join a small, hand-picked cohort with a dedicated research mentor, direct access to the team developing the algorithms for our in-memory inference hardware, and a clear arc from onboarding through publication. Every residency is built around two milestones: a research paper co-authored with our team, and a company-wide research colloquium where you present your findings. This track focuses on the computational methods that make LLM inference run efficiently on Normal's in-memory hardware, rethinking operations like attention and memory access for hardware that computes inside memory.

Responsibilities

Develop inference algorithms for in-memory hardware, understanding which parts of LLM workloads are best suited to in-memory execution and developing algorithms that exploit it natively.
Design numerical methods that fit the physics, exploiting thermal noise and analog dynamics.
Build evaluation suites and benchmarks to characterize algorithm behavior on real hardware and in simulation.
Translate workloads into hardware constraints for the hardware and architecture teams.
Prototype and iterate rapidly, moving with the hardware as it evolves from simulation to FPGA to silicon.
Drive a research question of your own, scoping, running, and iterating on an original technical investigation with rigorous experimental analysis and documentation.
Co-author a paper with the team for submission to a relevant venue.
Present your work and thinking at the residency colloquium to the broader Normal Computing research community.

Requirements

Currently pursuing or recently completed a graduate degree (MS or PhD, or equivalent research experience) in machine learning, applied mathematics, physics, electrical engineering, computer science, or a related field.
A working understanding of large model inference, including attention mechanisms, KV cache, long-context decoding, and memory bandwidth constraints.
Some exposure to inference optimization (quantization, sparsity, kernel fusion, or memory-efficient attention), or to stochastic systems, probabilistic methods, numerical analysis, or analog computation.
Strong Python skills.
Some experience with a systems language (C++, Rust, or similar) or with implementing algorithms close to hardware.
Some experience (course projects, research, or otherwise) turning a research idea into working, reasonably reproducible code.
Comfort reasoning from first principles about what a novel substrate can do efficiently.
Strong written and verbal communication skills.
A bias toward ownership and self-direction, energized by the ambiguity and speed of a small, fast-moving startup.

Skills

Python
C++
Rust
Machine Learning
Applied Mathematics
Physics
Electrical Engineering
Computer Science
Large Model Inference
Attention Mechanisms
KV Cache
Long-Context Decoding
Memory Bandwidth Constraints
Inference Optimization
Quantization
Sparsity
Kernel Fusion
Memory-Efficient Attention
Stochastic Systems
Probabilistic Methods
Numerical Analysis
Analog Computation
Algorithm Implementation
Research
Technical Writing
Presentation

Location

New York City
Silicon Valley (Mountain View)
London

Work Type

Residency Program

Experience Level

Resident

Education Level

Graduate Degree (MS or PhD, or equivalent research experience)

About the Company

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), and London.
Normal Computing is committed to providing reasonable accommodations to individuals with disabilities.

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.