toogeza - Machine Learning Engineer — Physics AI

• 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. • 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; • 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. • 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.