Machine Learning Engineer — Physics AI

Toogeza
Europe

Who this role is best for

Strong fit for candidates with scientific computing expertise who benchmark ML systems and collaborate on industrial partnerships.

Best fit for

  • Candidates with hands-on Physics AI experience and a background in scientific ML workloads
    — “Hands-on experience with Physics AI / scientific machine learning is required.”
  • Individuals who have designed controlled benchmarks for simulation or physical-science datasets
    — “Strong experimental methodology and ability to design controlled benchmarks.”
  • Professionals adept at integrating compressed datasets into PyTorch and distributed workflows
    — “Integrate compressed datasets into PyTorch and distributed training workflows.”
  • Those with experience in 3D/4D scientific data formats and their compression challenges
    — “Experience with large 3D/4D datasets such as volumetric grids, meshes, point clouds, or spatiotemporal fields.”
  • Individuals who publish technical findings and translate them into product strategies
    — “Turn experimental results into product recommendations and research directions.”

Things to consider

  • The role may involve collaboration with external research groups and industrial partners.
    — “Collaborate with external research groups and industrial partners on joint evaluations.”

How to stand out

  • Highlight experience benchmarking ML systems for scientific datasets in your resume.
    — “Benchmark our compression technology across a wide range of Physics AI architectures and datasets.”
  • Showcase your familiarity with neural operators, mesh GNNs, and PINNs in your application materials.
    — “Familiarity with architectures such as neural operators, mesh GNNs, transformers for physical systems, surrogate models, foundation models for science, PINNs or related methods.”
  • Demonstrate your understanding of GPU training performance and distributed workflows.
    — “Good understanding of GPU training performance, data loaders, profiling, and distributed training.”
  • Include examples of technical writing, such as blog posts or academic papers, in your portfolio.
    — “Write technical reports, benchmark publications, blog posts, and academic papers.”
Pace · SteadyCollaboration · MediumAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

Skills & requirements

Required

Python

Stack & domain

PythonPyTorchNeural OperatorsMesh GnnsTransformers For Physical SystemsSurrogate ModelsFoundation Models For SciencePinnsLeadershipCommunicationPhysics AIScientific Machine LearningCFDTurbulenceWeatherEngineeringScientific ComputingML SystemsData Compression

About the role

Original posting from Toogeza via Ashby

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.

If this role sounds like a fit — we’d love to hear from you! Just send over your CV and anything else you’d like us to consider.

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.

Source: Toogeza careers (Ashby)

Similar roles

More roles at Toogeza