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# Physics-Informed Machine Learning Engineer

- **Company:** Svitla Systems Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning, NumPy, Reliability Engineering, Pytorch, Pandas, Scikit Learn, Production Code, Data Analytics, Data Pipelines, Recurrent Neural Networks
- **Published:** October 3, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pou703d2mx

## About the Role

* 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.

Will be a plus

 * Experience in reliability engineering/PHM (prognostics and health management) background: RUL estimation, degradation modeling, accelerated-life testing.
 * Exposure to semiconductor or hardware degradation physics at a "read the literature critically" level.
 * Familiarity with nonlinear dynamics/recurrence or dynamical-systems features (e.g., RQA or comparable techniques).
 * Familiarity with hardware/datacenter telemetry or fleet analytics.
 * Experience working in small teams alongside domain scientists/mathematicians; comfortable turning research feedback into production code.

## Description

* 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.