Machine Learning Engineer

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.

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.

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.

• 1 - 4 years of relevant industry or applied ML experience preferred.

• Strong focus on applied machine learning and engineering implementation.

• Hands-on experience with Python and its ML ecosystem (NumPy, Pandas, Matplotlib, Scikit-Learn).

• Familiarity with at least one deep learning framework (PyTorch or TensorFlow).

• Solid understanding of software engineering principles (modular design, version control, testing, debugging).

• Experience structuring modular Python codebases.

• Familiarity with packaging, model serialization, and reproducible pipelines.

• Experience working within larger software systems (not only notebooks).

• Strong analytical and problem-solving skills.

• Good communication skills and ability to work in a collaborative team environment.

• Proficiency in English (reading and writing).

Nice to Have:

• 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.