AI Research Resident (Algorithms Codesign)

Develop inference algorithms for in-memory hardware. Understand which parts of LLM workloads are best suited to in-memory execution and develop algorithms that exploit it natively, demonstrated first in simulation and then on hardware.

Design numerical methods that fit the physics. Design methods that exploit thermal noise and analog dynamics rather than working around them.

Build evaluation suites and benchmarks. Help build the evaluation frameworks and benchmarks that characterize how algorithms behave on real hardware and in simulation, and tell us honestly whether any of it actually works.

Translate workloads into hardware constraints. Turn insights about model workloads into constraints and opportunities for the hardware and architecture teams.

Prototype and iterate rapidly. Move with the hardware as it evolves from simulation to FPGA to silicon.