Senior Machine Learning Engineer
Svitla Systems Inc. is looking for a Senior Machine Learning Engineer for a full-time position (40 hours per week) in Europe. Our client is a technology startup.
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) is preferred.
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 the available outcome labels.
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.
Regular performance appraisals to support your growth.
Flexibility in workspace, either remote or our welcoming office.
Bonuses for article writing, public talks, and other activities.
Generous time off, including vacation, national holidays, sick leaves, and family days.
Personalized learning programs tailored to your interests and skill development.
Free tech webinars and meetups organized by Svitla.
Regular corporate online activities.
Awesome team and a friendly, supportive community!