Physics-Informed ML Engineer: Degradation & Time-Series

Svitla Systems, Inc.; Argentina; Presencial; ARS 2.500.000 - 4.500.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Private medical insurance

Flexible workspace (remote or office)

Bonuses for referrals

Bonuses for article writing and talks

15 vacation days, 10 holidays, 10 sick

Descripción de la vacante

Svitla Systems, Inc. is seeking a Physics-Informed Machine Learning Engineer for a full-time role in Argentina.

You will build physics-informed models to predict degradation and health, using time-series data from sensors and telemetry, while combining physics-based losses with data-driven optimization. You will design a fusion layer that blends physics stress features with dynamical signals, and you’ll calibrate stress-proxy parameters and maintain the end-to-end data pipeline in Python.

Formación

• 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.

Responsabilidades

• Build the temporal model: design and train a physics-informed sequence model 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.
• Calibrate the physics-informed components: the stress-proxy parameters are currently engineering priors; you’ll help design and execute calibration strategies against available outcome labels.
• Harden the feature pipeline: the pipeline is Python/Pandas over time-aligned multi-sensor telemetry; extend and maintain it as modeling needs dictate.
• Write clear analysis docs and defend modeling choices to technical stakeholders and clients.

Conocimientos

Physics-informed ML

Time-series modelling

Bayesian calibration

Python data stack

Model interpretability

Educación

Master's degree in related field

Herramientas

PyTorch

JAX

Pandas

NumPy

scikit-learn

Descripción del empleo

Svitla Systems, Inc. is seeking a Physics-Informed Machine Learning Engineer for a full-time role in Argentina.

You will build physics-informed models to predict degradation and health, using time-series data from sensors and telemetry, while combining physics-based losses with data-driven optimization. You will design a fusion layer that blends physics stress features with dynamical signals, and you’ll calibrate stress-proxy parameters and maintain the end-to-end data pipeline in Python.