Top Machine Learning Jobs in Poland

Design, build and scale an automated translation-quality evaluation framework for localisation and dubbing. Implement production ML evaluation infrastructure, roll it out across teams, align metrics with product signals, and maintain/iterate systems in collaboration with R&D and cross-functional partners.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Build and own production ML and LLM systems for onboarding, risk, compliance, and operations. Design models, engineer features from messy data, balance performance and interpretability, integrate with backend training/inference pipelines, write tested code, and partner with product and operations to drive scalable, reliable automation.

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

The role involves conducting R&D in machine learning, particularly in model training and architecture development. Candidates should have a background in deep learning and experience in hands-on experiments with foundational models.

The role involves developing and supporting data processing pipelines, collaborating with ML Engineers, and improving infrastructure while mentoring team members.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

The intern will develop AI models, enhance neural mechanism understanding, and collaborate on generative modeling while maintaining code quality.

You will optimize ML infrastructure, automate ML pipelines, manage versioning, and collaborate with engineers to ensure model scalability and reliability.

Seeking a Machine Learning Engineer to operationalize ML models into scalable production systems, focusing on deployment, monitoring, and compliance with security and regulatory standards while collaborating with data scientists and product teams.

Conduct applied AI/ML research to solve home design challenges, work with raw data, test hypotheses, and build prototypes/MVP solutions.

The Senior ML Data Engineer will enhance data workflows, collaborate with teams to build datasets, influence data strategies, and implement pipelines for effective data usage in AI/ML products.

Lead large-scale ML projects overseeing their entire lifecycle. Influence technical decisions, design ML pipelines, and mentor other engineers.

The role involves building scalable ML models, mentoring engineers, translating business requirements into specs, and ensuring solutions meet performance constraints.

As a Senior AI/ML Engineer, you will design ML solutions, create datasets, architecture improvements for AI agents, and contribute to MLOps practices.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

The ML Ops/Data Engineer will ensure reliability and scalability of ML systems, manage data pipelines, monitor performance, and collaborate with teams for optimization.

The Senior ML Engineer will create and improve ML models, collaborate with teams, monitor models in production, and maintain documentation.

The role involves creating and improving ML models, collaborating with teams, developing experimentation roadmaps, and ensuring optimal performance of production models.

Design and scale predictive ML systems for VIS.X, taking end-to-end ownership of various high-impact ML initiatives and shaping the ML architecture.

The AI/ML Data Engineer develops solutions for ETL processes, implements data quality metrics, and adapts technologies for big data processing while leveraging AI/ML data platforms.

As a Research Engineer in ML, you'll optimize large-scale ML systems, collaborate with Research Scientists, enhance models, and implement algorithms in Python.

The Senior AI/ML Engineer will design and deploy AI systems for business impact, lead MLOps practices, and collaborate with teams to drive AI solutions.

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.