Machine Learning Engineer — Physics AI

The role

You will work at the intersection of Physics AI, scientific computing, ML systems, and data compression. You will benchmark compression technology across Physics AI architectures and run large-scale experiments for scientific ML workloads.

What you will do

• Benchmark compression technology across 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, accuracy, throughput, GPU utilization, and storage.
• 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.

Required qualifications

• 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.

About the company

toogeza is a Ukrainian recruiting company focused on hiring talent and building teams for tech startups worldwide. Zibra AI is a deep-tech company building advanced technologies for working with large-scale 3D data.