Physics-Informed Machine Learning Engineer

Svitla Systems, Inc.

Що робити

Build the temporal model: design and train a physics-informed sequence model (e.g., LSTM or similar temporal architecture) for degradation and health prediction, incorporating a physics-based loss term alongside the data-driven loss.
Design the fusion layer: define how physics-based stress features, dynamical/mathematical features, and other signals combine into model inputs and a defensible health score, replacing today’s simple hand-set weighting.
Calibrate the physics-informed components: the stress-proxy parameters are currently engineering priors. You’ll help design and execute calibration strategies against whatever outcome labels are available.
Harden the feature pipeline: the pipeline is Python/Pandas over time-aligned multi-sensor telemetry; you’ll extend and maintain it (feature audits, label engineering, data-quality gates) as modeling needs dictate.
Write clear analysis docs and defend modeling choices to technical stakeholders and clients.

Experience in building and training physics-informed models — a physics-based term in the loss function of a real project (PINN, physics-regularized NN, or equivalent).
Strong understanding of time-series/sequence modeling (LSTM, temporal CNN, transformers, or state-space models) on sensor or telemetry data.
Understanding of parameter calibration / inverse problem: fitting mechanistic model parameters to noisy observational data (Bayesian calibration, MLE, or optimization-based).
Expert knowledge of Python scientific stack (Pandas, NumPy, scikit-learn, PyTorch or JAX) and be comfortable owning a data pipeline end to end, including data-quality investigation.
Expertise in reading and reasoning about physics/reliability equations governing degradation; you don’t need to derive them, but they can’t be a black box.

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