Applied Machine Learning Engineer

Destinus

. Build ML models that predict residual sensor error using observable signals including temperature, thermal gradients, quadrature amplitude, drive signals, and sensor diagnostics .

Posted 9/18/2026full-timeZürich • SwitzerlandMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and validating machine learning models for sensor data, with a strong foundation in Python or MATLAB and experience in deploying models to embedded systems. Possesses a solid understanding of physics and signal processing to ensure meaningful data interpretation and model generalization.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingModel Validation and GeneralizationSensor Calibration and MetrologyEmbedded Systems Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningTime-Series AnalysisRegression AnalysisFeature EngineeringExperimental DesignC/C++ ProgrammingSignal ProcessingData Quality AssessmentModel Overfitting MitigationKalman Filtering

Soft Skills

Clear CommunicationCollaboration with Engineers

Tools & Technologies

PyTorchScikit-learnMATLABFPGADSP

Industry Keywords

AerospaceDefenceRoboticsHigh-Performance Engineering

Tech Stack

Tools & technologies

PythonPyTorchScikit-LearnC++

About the role

Key responsibilities & impact

Build ML models that predict residual sensor error using observable signals including temperature, thermal gradients, quadrature amplitude, drive signals, and sensor diagnostics
Define rigorous validation protocols across unseen thermal profiles and physical sensor units to demonstrate genuine generalisation
Benchmark learned approaches against a tuned classical baseline combining per-unit thermal compensation and adaptive Kalman filtering
Quantify the observability boundary and identify which errors are predictable from available measurements and which are fundamentally outside the model's reach
Train and evaluate models offline using sensor characterisation data, separating meaningful physical correlations from artefacts and overfitting
Work with FPGA and DSP engineers to translate successful approaches into lightweight, frozen models suitable for low-latency embedded deployment
Communicate results clearly, including when the evidence shows that a classical approach remains the better solution

Requirements

What you’ll need

B.Sc., M.Sc. or PhD in Computer Science, Applied Mathematics, Applied Physics, Machine Learning, or a related technical field
Strong applied machine learning experience with time-series, regression, sensor, or instrumentation data
Strong Python or MATLAB skills and experience with PyTorch, scikit-learn, or equivalent modelling frameworks
Experience working effectively with modest, high-value datasets where validation strategy and data quality matter as much as model architecture
Strong understanding of model validation, generalisation, overfitting, feature engineering, and experimental design
Enough physics and signal-processing knowledge to challenge whether a discovered relationship is physically meaningful or simply an artefact in the data
C/C++ skills are highly desirable
Experience with sensor calibration, metrology, inertial sensing, or similar physical measurement systems is a strong advantage
Experience deploying ML models to embedded or resource-constrained targets is a plus
Exposure to aerospace, defence, robotics, or other high-performance engineering environments is a plus