Senior Machine Learning Engineer

Aether Biomedical; Deutschland; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Benefits dieser Stelle

Flexible workspace

Tech webinars

Team events

Zusammenfassung

Svitla Systems Inc. seeks a Senior Machine Learning Engineer for a full-time role in Europe.

You will design and train physics-informed sequence models (e.g., LSTM/temporal architectures) for degradation and health prediction, integrating physics-based loss terms with data-driven objectives. You will build fusion layers that combine physics stress features with data signals, calibrate model priors, and maintain Python/Pandas pipelines.

Qualifikationen

• Experience in building and training physics-informed models (PINN or physics-regularized NN).
• Strong time-series/sequence modeling skills on sensor or telemetry data.
• Familiarity with parameter calibration / inverse problems (Bayesian, MLE, or optimization).
• Expertise in Python scientific stack and end-to-end data pipelines.

Aufgaben

• Build a physics-informed temporal model for degradation and health prediction.
• Design fusion layers combining physics-based features with data signals.
• Calibrate physics-informed components against available labels.
• Harden the feature pipeline (data quality gates, label engineering).
• Write analysis docs and defend modeling choices to technical stakeholders.

Kenntnisse

Physics-informed ML

Time-series modeling

Python stack

Data pipeline

Model calibration

Tools

PyTorch

JAX

Pandas

NumPy

scikit-learn

Jobbeschreibung

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 offerUS 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!