Machine Learning Engineer

Si ware systems

You will work at the intersection of machine learning, software engineering, and intelligent hardware integration.

Responsibilities:

Machine Learning & Modeling

• Perform data cleaning, preprocessing, and transformation for training and evaluation.

• Contribute to the development, training, and improvement of machine learning models.

• Design and execute structured experiments using appropriate validation strategies (cross-validation, hold-out testing, statistical comparison).

• Evaluate models using appropriate performance metrics (Accuracy, Precision, Recall, F1, RMSE, etc.).

• Optimize models and inference pipelines for performance, memory efficiency, and real-time constraints when required.

• Ensure reproducibility, traceability, and proper validation of models within regulated or industrial environments.

• Support chemometrics-related workflows, including spectral preprocessing, feature extraction, multivariate modeling, and validation for spectroscopy-based applications.

Production & System Integration

• Design and implement production-ready ML pipelines integrated with software applications and hardware systems.

• Ensure models are maintainable, version-controlled, and deployable within real-world products.

• Contribute to the design and evolution of ML system architecture and reusable pipeline components.

• Develop unit and integration tests for ML components to ensure reliability.

• Document ML modules and interfaces to support long-term maintainability.

• Work closely with software and firmware teams to integrate ML models into desktop applications, services, and embedded workflows.

Tools, Innovation & Growth

• Participate in the development of internal and customer-facing ML-driven tools and automation utilities.

• Stay updated with the latest machine learning research, tools, and industry practices.

• Contribute to exploratory and applied ML solutions in emerging physical AI systems, including robotics and sensor-driven intelligent platforms.