Machine Learning Engineer — Physics AI

Toogeza; Eu; Hybrid; EUR 70,000 - 110,000; Full time

Job summary

toogeza is seeking a Machine Learning Engineer — Physics AI for Zibra AI, focusing on benchmarking and improving compression for large-scale scientific ML.

You will work with CFD, turbulence, weather, and other physics workflows to build reproducible benchmarks and collaborate with researchers.

Qualifications

• Hands-on experience with Physics AI / scientific ML.
• Experience training models on simulation or physical-science datasets.
• Strong practical experience with PyTorch and modern DL workflows.
• Experience with large 3D/4D datasets (volumetric grids, meshes, point clouds).
• Good understanding of GPUs, data loaders, and distributed training.
• Strong Python and scientific computing skills.
• Ability to communicate results in technical writing.

Responsibilities

• Benchmark compression across Physics AI architectures and datasets.
• Run large-scale CFD, turbulence, weather, and other scientific ML experiments.
• Measure effects of compression on convergence, accuracy, throughput, GPU utilization.
• Compare compressed-data training with conventional pipelines.
• Research training directly in compressed representations.
• Design compression-aware sampling, augmentation, tokenization, and models.
• Integrate datasets into PyTorch and distributed workflows.
• Publish benchmarks and collaborate with external partners.
• Write technical reports and papers.
• Turn results into product recommendations.

Skills

Physics AI / scientific ML

PyTorch

Python

Scientific computing

Large 3D/4D datasets

Experimental benchmarking

Job description

We are toogeza, a Ukrainian recruiting company focused on hiring talent and building teams for tech startups worldwide. People make a difference in the big game, and we may help find the right ones.

Currently, we are looking for a Machine Learning Engineer — Physics AI for Zibra AI.

Zibra AI is a deep-tech company building advanced technologies for working with large-scale 3D data. The team has a strong background in computer graphics and data compression and is now expanding its technology into industrial simulation and Physics AI.

The company is developing a new data infrastructure layer that makes massive scientific and simulation datasets significantly easier to store, transfer, visualize, and use for AI training.

You will work at the intersection of Physics AI, scientific computing, ML systems, and data compression. A major part of the role is to benchmark our codec across different model architectures, study how compression affects accuracy and training efficiency, and explore new approaches to training directly in compressed representations.

What you will do

• Benchmark our compression technology across a wide range of Physics AI architectures and datasets.
• Run large-scale experiments for CFD, turbulence, weather, engineering, and other scientific ML workloads.
• Measure the impact of compression on:
  • model convergence and final accuracy;
  • training throughput;
  • GPU utilization;
  • CPU and data-loading overhead;
  • storage and network requirements.
• Compare compressed-data training against conventional pipelines and alternative compression methods.
• Research training directly in compressed or partially decoded representations.
• Explore compression-aware sampling, augmentation, tokenization, and model architectures.
• Design rigorous, reproducible benchmark methodology.
• Integrate compressed datasets into PyTorch and distributed training workflows.
• Turn experimental results into product recommendations and research directions.
• Write technical reports, benchmark publications, blog posts, and academic papers.
• Collaborate with external research groups and industrial partners on joint evaluations.

What we are looking for

• Hands-on experience with Physics AI / scientific machine learning is required.
• Experience training models on simulation or physical-science datasets.
• Strong practical experience with PyTorch and modern deep-learning workflows.
• Familiarity with architectures such as:
  • neural operators;
  • mesh GNNs;
  • transformers for physical systems;
  • surrogate models;
  • foundation models for science;
  • PINNs or related methods.
• Experience with large 3D/4D datasets such as volumetric grids, meshes, point clouds, or spatiotemporal fields.
• Good understanding of GPU training performance, data loaders, profiling, and distributed training.
• Strong experimental methodology and ability to design controlled benchmarks.
• Ability to analyze how numerical approximation and preprocessing affect model quality.
• Strong Python and scientific-computing skills.
• Ability to communicate research results clearly in written technical form.

Nice to have

• Experience with PhysicsNeMo or similar scientific ML frameworks.
• Background in CFD, FEA, climate, turbulence, combustion, or computational physics.
• Knowledge of lossy compression, quantization, numerical error analysis, or signal processing.
• Multi-GPU or multi-node training experience.
• Previous academic publications in ML, scientific computing, compression, or related fields.

We’ll review everything within five working days, and if your background matches what we’re looking for, we’ll get in touch to set up a call and get to know each other better.