Technical Product Owner — Scientific Data Platform
Toogeza · Europe
We are toogeza, a Ukrainian recruiting company that is focused on hiring talents and building teams for tech startups worldwide. People make a difference in the big game, we may help to find the right ones.
Currently, we are looking for a Technical Product Owner for Zibra A.
About our client
Zibra AI is a deep-tech company building data infrastructure for spatial and physical AI. With deep expertise in 3D data compression, GPU technologies, and real-time data pipelines, the team develops technology that enables large-scale 3D datasets to be stored, streamed, visualized, and used for AI workflows more efficiently.
Product Overview
Zibra AI is building the data infrastructure layer for Physics AI — bringing streaming, random access, and GPU-native processing to massive 3D scientific datasets. Our goal is to make petabyte-scale simulation data as easy to access and work with as video is today.
Role Overview
We are looking for a Technical Product Owner to take ownership of a new platform for large-scale scientific and engineering data.
This role is ideal for someone who understands simulation workflows firsthand and has moved from engineering, scientific computing, or technical software into product ownership. We are particularly interested in people with backgrounds in ParaView/VTK, CFD, FEA, CAE, simulation software, scientific visualization, HPC, or engineering data platforms.
You will work closely with engineering and research teams to define the product, prioritize the roadmap, translate complex technical requirements into clear product decisions, and ensure that what we build fits real engineering workflows.
What you will do
Own the product roadmap and priorities for the platform.
Define workflows for scientific data ingestion, visualization, collaboration, sharing, and AI model training.
Work closely with C++, backend, frontend, GPU, and visualization engineers.
Translate customer and user needs into detailed product requirements and acceptance criteria.
Design integrations with tools such as ParaView, Python, PyTorch, object storage, and simulation software.
Work with engineering teams on data models, APIs, file-format support, streaming, and interoperability.
Interview users across automotive, aerospace, energy, research, and other simulation-heavy industries.
Define MVP scope, release priorities, and product success metrics.
Evaluate open-source technologies and decide where to integrate, extend, or build proprietary components.
Help shape the long-term product strategy for large-scale engineering and Physics AI data workflows.
What we are looking for
We are open to several types of profiles:
A technical product manager / product owner with experience in simulation, scientific computing, CAE, HPC, or visualization software.
A former simulation engineer who moved into product, solutions architecture, technical program management, or product leadership.
A ParaView / VTK / scientific visualization engineer who wants to move into a broader product ownership role.
A simulation engineer or technical lead with strong customer-facing and product experience.
You should have:
Strong understanding of engineering or scientific workflows.
Experience with CFD, FEA, CAE, scientific visualization, HPC, or related domains.
Ability to communicate effectively with both engineers and customers.
Experience defining technical products, APIs, workflows, or platform capabilities.
Strong product judgment and ability to prioritize in an early-stage environment.
Ability to understand complex technical architecture without needing to implement every component yourself.
Nice to have
Hands-on experience with ParaView, VTK, PyVista, or similar tools.
Experience with simulation software such as Ansys, STAR-CCM+, OpenFOAM, Altair, or similar.
Knowledge of meshes, volumetric grids, point clouds, simulation fields, and time-dependent datasets.
Familiarity with Zarr, HDF5, VTK formats, and scientific data infrastructure.
Experience with cloud HPC or platforms such as Rescale, SimScale, or similar.
Familiarity with machine learning for engineering, surrogate models, or Physics AI.
Experience working with open-source technical ecosystems.
Previous experience in an early-stage or highly technical product environment.