Machine Learning Engineer

About Rivach

Rivach is an AI-native technology company and innovation studio building intelligent products, software systems and applied AI solutions.

About the role

Rivach is looking for a Machine Learning Engineer who can work across experimentation, model development and production engineering. You will help design machine learning systems, build reliable training and evaluation pipelines, work with real-world datasets and deploy models into products and operational workflows. The role sits at the intersection of machine learning, data and software engineering.

What you'll work on

• Develop and evaluate machine learning models for real-world product and business problems.
• Build reproducible data preparation, training, validation and evaluation pipelines.
• Work with structured and unstructured datasets to identify useful signals and improve model performance.
• Productionize models through APIs, services or batch and streaming workflows.
• Monitor model quality, performance and reliability after deployment.
• Collaborate with software and AI engineers to integrate machine learning capabilities into products.
• Run disciplined experiments and communicate findings, trade-offs and recommendations.
• Improve internal tooling and reusable machine learning infrastructure.

What we're looking for

• Strong Python programming skills.
• Solid understanding of machine learning fundamentals, model evaluation and experimentation.
• Experience with common ML libraries and frameworks such as scikit-learn, PyTorch or TensorFlow.
• Experience working with data processing, feature engineering and model pipelines.
• Understanding of APIs, databases and production software systems.
• Ability to reason about model quality, data quality and failure modes rather than optimizing metrics blindly.
• Strong analytical and problem-solving skills.

What would make you stand out

• Experience deploying models to cloud or containerized environments.
• Experience with MLOps, experiment tracking, model registries or monitoring.
• Experience with NLP, recommendation systems, forecasting, ranking or deep learning.
• Experience working with large-scale data processing systems.
• Familiarity with modern generative AI and foundation-model workflows.
• Research, open-source or technically substantial ML project experience.