Top Machine Learning Jobs in Krakow

The role involves designing and implementing machine learning solutions, managing projects from idea to deployment, and optimizing models using AWS and Docker.

Lead a team of ML Engineers to develop and optimize ML systems, ensuring high-quality clinical care through AI. Foster collaboration across teams for innovative AI/ML features.

As a Senior Data Scientist at Chaos Labs, you'll enhance modeling capabilities and deliver insights for decentralized finance, collaborating with engineering and product teams.

Lead the AI compiler team to develop compilers for NextSilicon's platforms, leveraging expertise in large-scale compiler development and machine learning.

Lead the development of GenAI infrastructure, build ML platforms, contribute to LLM testing and deployment, and collaborate across teams to improve AI-driven experiences.

As an ML Engineer II, you will develop and improve core components of Zendesk's GenAI infrastructure, focusing on LLM applications, benchmarking frameworks, and orchestration tools for AI agents while collaborating with cross-functional teams.

This role involves designing, developing, and maintaining AI/ML software solutions, proposing new optimization approaches, and collaborating with global teams to solve real-world challenges.

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

The Sr. Analytics Manager will guide the analytics team, support strategic projects, and deliver insights through data storytelling and advanced analytics.

Develop and implement Generative AI applications, optimize data solutions, and advise clients while using MLOps practices and ensuring security standards.

The Senior Data Engineer will design and implement scalable data architectures, manage data flows, and collaborate with multidisciplinary teams to enhance data applications.

The Staff Machine Learning Engineer will enhance Zendesk's search platform, deliver AI capabilities, mentor team members, and ensure service stability. Responsibilities include collaborating on feature development, technical discussions, and addressing challenges in retrieval systems and LLM technologies.

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

Responsible for developing backend AI features, integrating machine learning models, building data pipelines, and maintaining AI services within a retail ecosystem.

Seeking a seasoned data scientist to advance methodologies for cardiovascular health assessment, collaborating with medical experts and algorithmic team.

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

Lead a cross-functional team in developing and maintaining the MLOps infrastructure at Zyte. Responsibilities include managing distributed systems, optimizing model platforms, and overseeing team operations.

The Senior Product Manager - Capture leads AI-based invoice capture and validation, driving strategy and collaboration across teams to optimize document processing for accounts payable automation.

Develop and implement machine learning models for lead scoring, customer segmentation, and optimization, while collaborating across teams to drive data-driven strategies.

As an ML Engineer, you will develop ML models, collaborate with teams, maintain model performance, and drive innovation in ML technologies.

Develop and integrate machine learning solutions for fraud prevention in transactions, analyze data using SQL and Python, and manage the complete lifecycle of ML solutions.

Research and implement physics-informed AI algorithms, design workflows, supervise teams, collaborate with external institutes, and publish findings.

As an ML Engineer at Provectus, you'll create and improve ML models, collaborate on production efforts, develop experimentation roadmaps, and maintain model performance.

As an ML Engineer, create and improve ML models, collaborate with teams, maintain experimentation pipelines, and stay updated with ML advancements.

Lead and manage high-performing teams developing ML/AI platforms, focusing on LLM infrastructure and collaboration across departments.