Role overview
Si-Ware Systems is a global leader in semiconductor and spectroscopy solutions. Our innovative devices and software enable material analysis across many industries.
At Si-Ware, we foster a culture of innovation, collaboration, and continuous learning, empowering our people to push the boundaries of technology.
As a Machine Learning Engineer at Si-Ware, you will design, implement, and deploy applied ML solutions that power real-world spectroscopy devices and emerging physical AI systems.
You will work at the intersection of machine learning, software engineering, and intelligent hardware integration.
initiatives.
What you'll work on
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.
What we're looking for
- Basic understanding of chemometrics concepts (multivariate analysis, regression/classification, spectral data handling).
- Exposure to spectroscopy data (NIR, IR) and common preprocessing techniques (normalization, smoothing, baseline correction).
- Experience contributing to ML tools, internal platforms, or data analysis software.
- Knowledge of MLOps practices (Git, CI/CD, Docker).
- Experience with cloud platforms (AWS, GCP, Azure).
- Experience with robotics frameworks (ROS).
- Experience with sensor fusion.
- Experience with real-time ML inference.
- Basic understanding of control systems.
- Exposure to data visualization or BI tools.
- Contributions to Kaggle competitions, research projects, or open-source initiatives.