layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Member of Technical Staff, Performance Engineering at Physical Superintelligence | LAYIQ

Whole-stack performance ownership. You have taken a workload from slow to fast across more than one layer, with a trace that shows where the time went before and after, and at least one of those layers was not the GPU: a data loader, a filesystem, a scheduler, a container start, a service call, a Python hot loop. You work from measurements, instrument systems you did not write, and verify a win on a real workload before you claim it.
Kernels you wrote yourself. Five or more years making GPU code fast, including CUDA C++ or Triton kernels that other people's systems depend on. You read Nsight Compute output the way a physicist reads a plot, and you can say where a kernel sits on the roofline and why.
Scale on at least one side, and the appetite for the other. Either real multi-node AI training experience (distributed training throughput, collective-communication behavior, InfiniBand topology, where step time goes and why MFU disappoints) or HPC simulation at scale (a CFD, molecular dynamics, multiphysics or comparable code taken across nodes with MPI, scaling measured, solver kernels ported or tuned on GPUs, FP64 and extended-precision numerics). Half the demand on this cluster is simulation and physics numerics and half is AI; you bring one and learn the other on our hardware.
You can explain a performance result to a researcher who will never open a profiler, and your numbers are usable by people who make decisions with them.
Nice to Have
Both sides of the scale requirement: multi-node AI training and HPC MPI simulation.
Inference serving for large open-weight models: vLLM, SGLang or comparable, multi-GPU placement of mixture-of-experts models, KV-cache and quantization trade-offs.
Source-level work in OpenFOAM, LAMMPS, GROMACS, Nek5000, or comparable codes; AMR or stencil frameworks; PETSc, Trilinos, MAGMA or comparable libraries.
CUTLASS, CuTile, or comparable kernel frameworks; compiler-level work in Triton or MLIR.
Kueue or Slurm queue policy on a shared cluster: priority classes, preemption, bin-packing, and the before-and-after that justified a policy change.
You have owned a capacity decision with money attached and been held to the number.
Experience turning research code into a maintained library that a team of scientists depends on.
How We Work
We hold a high technical bar and give people full ownership of their work, from spec to ship to on-call. We write contracts before logic, test against real systems instead of mocks, and favor simple designs that ship over clever ones that do not. Our development process is AI-native: we work with agentic coding tools daily, write specs that are legible to humans and agents alike, and lead with leverage.
Location and Compensation

Member of Technical Staff, Performance Engineering

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