Top Machine Learning Jobs in Krakow

The Director of Applied Machine Learning will lead a team focused on model development and productionization, collaborating with cross-functional teams to deliver impactful ML solutions in life sciences and healthcare.

The Senior ML Infrastructure / DevOps Engineer will design, operate, and scale infrastructure for machine learning workloads, automating provisioning and building robust ML pipelines while collaborating with ML teams for production readiness.

The Staff Machine Learning Engineer will oversee the ML aspects of Zendesk's routing products, ensuring quality and interoperability, while collaborating with various teams, leading innovation, and advancing ML literacy across the organization.

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

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 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.

Design and build simulation environments for robotics research, implement RL algorithms, and evaluate policies for stability and transfer potential. Collaborate with teams and document results.

Drive inference optimization by improving throughput and latency, managing KV cache utilization, and optimizing LLM performance and configuration. Lead technical direction and address bottlenecks in distributed systems.

Responsible for delivering AI solutions through the full product lifecycle, collaborating with teams to align technology and business needs, ensuring best practices.

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

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

Lead the Applied AI Team in developing and deploying ML systems for clinical care, mentoring engineers, and driving initiatives across the AI stack.

Own and drive ML solutions for clinical care at scale, including LLM fine-tuning and production deployment, collaborating across teams.

The Senior ML Engineer will design, build, and optimize ML models, focusing on recommender systems and MLOps, primarily using AWS.

Develop and enhance AI-driven solutions using machine learning in Palantir Foundry, collaborate on workflows, and support model deployment.

The Program Manager oversees operational quality in ML Data Operations, establishes quality metrics, designs annotation frameworks, and supports various teams with quality insights. Their role ensures effective decision-making and enhances customer experience through operational excellence.

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.

The Machine Learning Engineer II will develop and optimize ML models, process large datasets, and integrate ML solutions while collaborating with peers and staying up to date on technologies.

The role involves designing AI agents for operations, developing ML models, and integrating solutions into workflows while collaborating with teams.

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

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

As a Senior Machine Learning Engineer, you will design and deploy AI models, work on data pipelines, and enhance AI-driven retail solutions through collaboration with various teams.