layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

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

Member of Technical Staff, Data Systems at Physical Superintelligence | LAYIQ

Five or more years building data infrastructure in production at companies known for engineering rigor. A data generalist: databases, storage systems, filesystems, catalogs, and the data platforms other teams built on every day. An engineer who scales systems, not an administrator who operates them. Platform ownership, not pipeline authorship.
You think in data identity: immutable content, names as pointers to versions, provenance that cannot drift from the data it describes. You know what lineage costs when it is an afterthought, and how rights and retention get enforced in the data path rather than in a policy document.
You have watched a catalog, namespace, or metadata service break before the storage under it did. You can explain what you changed.
You can produce a replicate-versus-fetch break-even from memory, argue a tiering decision in dollars, and defend a placement policy to the person paying the bill.
Nice to Have
Content-addressed storage, data versioning, or lineage systems in production, whether built on object-store internals or from scratch.
Checkpointing at GPU scale: absorbing the burst when a training run dumps state from every card at once, and making restore fast enough to matter.
Distributed-systems depth alongside the data work: schedulers, workflow engines, high-volume services. Our data and execution work sit next to each other, and the engineer strong in both is the one we most want to meet.
Experience with scientific or research data: instrument output, simulation results, dataset licensing, or archives meant to be readable a decade later.
Table formats and catalogs at scale, and the economics of multi-cloud data placement.
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, Data Systems

Employer job description

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