Svitla Systems Inc. is looking for a Physics-Informed Machine Learning Engineer for a full-time position (40 hours per week) in Argentina.

Requirements

- 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.

Nice to have

- 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.

Responsibilities

- 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.

WE OFFER

- US and EU projects based on advanced technologies.

- Competitive compensation based on skills and experience.

- Comprehensive private medical insurance.

- Regular performance appraisals to support your growth.

- Flexibility in workspace, either remote, our welcoming office or local coworking.

- Bonuses for recommendations of new employees.

- Bonuses for article writing, public talks, other activities.

- 15 vacation days, 10 national holidays, 10 sick leaves.

- Personalized learning program tailored to your interests and skill development.

- Free tech webinars and meetups organized by Svitla.

- Fun corporate onlineoffline celebrations and activities.

- Well-established remote culture.

- Awesome team, friendly and supportive community!

📌 PHYSICS-INFORMED MACHINE LEARNING ENGINEER (Argentina)
🏢 Svitla Systems
📍 Argentina