layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

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

Member of Technical Staff, Applied AI at Physical Superintelligence | LAYIQ

A PhD, or an equivalent research record, in mechanical, electrical, aerospace, or chemical engineering, applied physics, computational science, or machine learning for science. You have modeled a physical system that mattered and validated the model against ground truth: a surrogate against a solver, a simulation against a facility, a model against measured data. You can say how you knew it was right and what you did when it was not.
Depth in at least one engineering domain. Thermal and fluid (CFD, conjugate heat transfer), power systems (grid interconnection studies for large loads including ride-through, protection, and the dynamics of UPS, BESS, and converters), structural (FEA), or electrochemistry, with the tools of that domain (OpenFOAM, SU2, ETAP, PSS/E, PSCAD, OpenDSS, pandapower, CalculiX, Code_Aster, or comparable) and the standards it works to. You know where the models lie.
Hands-on computational engineering. You have run and extended CFD, FEA, power system, or multiphysics codes, you understand discretization, convergence, and boundary conditions, and you know the trade-offs between a high-fidelity solver and a faster learned alternative.
Machine learning you have applied yourself: PyTorch or JAX, training runs you designed and debugged, models that held up outside the training set. You do not need a top-venue publication record. You do need to have shipped ML on a physics problem rather than on a synthetic benchmark.
You have put a model into a system other people depend on: behind an API, versioned, reproducible, with tests that catch a wrong number and not just a crash. You are comfortable in HPC and cloud GPU environments.
Judgment on real-world problems. You can scope a problem with a senior domain engineer, tell them what is credible and what is not, and deliver working artifacts rather than slide decks.
Nobody is expected to be expert in all of the above. We hire for strengths, not for the absence of weaknesses: strong in most of it, deep in at least one domain. You start on an open-ended problem without waiting to be told how, pick up an unfamiliar domain when the work needs it, are comfortable with ambiguity, and work well across disciplines and as part of a team.
Nice to Have
Neural operators, physics-informed networks, differentiable simulation, or uncertainty quantification for learned models.
Experience with open-source simulation codes (OpenFOAM, OpenDSS, pandapower, CalculiX, or comparable) rather than only commercial packages.
Domain work in thermal and fluid systems, power systems and power electronics, structures, or electrochemistry.
Optimization over design spaces: mixed-integer, derivative-free, or Bayesian search.
Peer-reviewed publications in computational physics, engineering, or ML for science.
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, Applied AI

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