Top Machine Learning Jobs in Poland

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead and manage a team of ML engineers and researchers to design, deploy, and monitor scalable computer vision, graphics, and generative AI models for a VFX platform. Define ML strategy, collaborate with product and engineering to integrate models into production pipelines, ensure responsible AI practices, and partner with infrastructure teams to build efficient training and deployment systems.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

Design and develop software solutions for AI/ML applications, propose new approaches to optimization problems, and collaborate with global teams in research and development.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

Maintain and scale production ML systems: monitor model performance and explainability, automate MLOps pipelines, troubleshoot production workflows, build feature and inference monitoring, perform EDA on historical time-series data, and support internal tools and deployment-specific adjusters.

Maintain and scale production ML systems: monitor model performance and explainability, automate MLOps pipelines, troubleshoot production workflows, perform EDA and data quality checks, build and maintain static and temporal features, support feature engineering and artifact monitoring, and develop internal tools and registries to ensure reliable, explainable ML deployments.

Support and maintain production ML systems: monitor performance and explainability, automate MLOps pipelines, troubleshoot production issues, build and maintain feature and monitoring repositories, perform EDA, implement production adjusters, and support internal tooling and feature engineering for time-series forecasting.

As a Principal AI/ML Engineer, you will lead AI initiatives, architect ML systems, build PoCs, collaborate with data engineers, and mentor team members, focusing on scalable solutions across various domains.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

As a Senior Data Engineer, you'll design scalable ML pipelines, collaborate with teams, ensure data quality, and mentor junior engineers.

As a Senior Data Engineer, you will design and maintain ML applications, manage workflows, build data pipelines, and mentor junior engineers within a global team.