We are building a Physics-Informed Foundational Model to understand GPU and compute health. We derive physics-grounded stress signals: effective-stress proxies, semiconductor degradation estimates, and dynamical mathematical features, and use them to assess hardware health over time. The feature pipeline runs end to end. You'll be building the model and fusion layer on top of it.
You will own the modeling: designing the fusion layer (how physics-based and dynamical features combine into a coherent health signal) and the temporal modeling layer (a physics-informed model, PINN-style, where empirical stress signals drive part of the loss and a physics-based degradation model informs another part).
The exact formulation of the physics term is still evolving, you'll be involved in shaping it. You'll work as part of a small, technical team alongside the founder and other domain experts.

What you'll do
- 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: our 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.
- Communicate: write clear analysis docs and defend modeling choices to technical stakeholders and clients.

Must-haves
- Has actually built and trained physics-informed models a physics-based term in the loss function of a real project (PINN, physics-regularized NN, or equivalent)
- Strong time-series / sequence modeling experience (LSTM, temporal CNN, transformers, or state-space models) on sensor or telemetry data.
- Parameter calibration / inverse-problem experience: fitting mechanistic model parameters to noisy observational data (Bayesian calibration, MLE, or optimization-based).
- Fluent Python scientific stack (pandas, NumPy, scikit-learn, PyTorch or JAX) and comfortable owning a data pipeline end to end, including data-quality investigation.
- Able to read and reason about physics/reliability equations governing degradation, you don't need to derive them, but they can't be a black box.
Solid pluses
- 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.
- Nonlinear dynamics / recurrence or dynamical-systems features (e.g. RQA or comparable techniques).
- Experience with hardware/datacenter telemetry or fleet analytics.
- Worked in small teams alongside domain scientists/mathematicians; comfortable turning research feedback into production code

📌 Physics-Informed Machine Learning Engineer (Pune)
🏢 Svitla Systems
📍 Pune