Top Artificial Intelligence Jobs in Poland

As an AI Inference Engineer, you will optimize C++ systems for AI model inference on edge devices, collaborating with researchers to deploy and enhance models while ensuring runtime stability and performance.

Edit and produce high-performing marketing videos, collaborate on concepts, optimize content for various platforms, and leverage AI tools for enhanced creativity.

Design and implement cloud-native AI solutions, build relationships with clients, and optimize AI systems. Mentor others and lead architecture decisions.

The AI Go-to-Market Engineer will deploy and optimize AI systems across revenue operations to enhance productivity, pipeline generation, and minimize churn, reporting directly to the CEO.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.

Design, build, and scale intelligent systems using AI/ML across various domains including LLMs, Computer Vision and Speech AI, integrating these into business workflows.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

AI Engineers at QuantCo design, build, and implement applied AI systems, working with data scientists to optimize inference, develop scalable solutions, and integrate AI into enterprise operations.

As an Early Career Software Engineer with AI at Egnyte, you'll develop AI-based solutions, work in teams, collaborate cross-functionally, and be mentored by a Senior Engineer.

Perform remote, part-time data tasks to improve AI Polish content: collect, evaluate, annotate data; perform pairwise comparisons, counting, object tagging and labeling across audio, video, images and collected data.

Design, build, and operationalize AI agentic solutions across the software lifecycle. Own backend responsibilities, contribute to frontend, define technical requirements, deliver maintainable code, collaborate with clients, and stay current with AI, LLM orchestration, and evaluation practices.

Serve as a senior AI advisor delivering AI/GenAI projects: define architectures and best practices, lead technical teams, create DS/AI offerings, support sales and delivery, evangelize AI across clients and the company, research market trends, and ensure high customer satisfaction throughout the project lifecycle.

The Principal Software Engineer will build customer-facing AI Functions, design scalable platforms, collaborate across teams, and ensure service reliability.

The Senior Backend Engineer will develop and enhance banking solutions, own the software lifecycle, and collaborate across teams to implement best practices.

The Python Developer will design and build AI/ML automation solutions, integrate models, develop CI/CD pipelines, and maintain APIs. Collaboration with Data Scientists and Engineers is key.

The Senior Product Manager will lead AI-focused product strategies for internal and external developer experiences, oversee product lifecycle management, and mentor junior team members while leveraging data and AI technologies to enhance developer productivity and satisfaction.

The AI Integration Specialist will drive AI adoption, improve internal operations, audit tools, lead initiatives, and manage vendor relationships to enhance business efficiency.

The AI Engineering Manager will build and lead a high-performing team focused on AI agents, workflow orchestration, and navigating evolving technology, ensuring clarity and delivering impactful results.

Lead design and delivery of end-to-end ML/AI solutions: architect systems, implement and validate models, oversee data pipelines, coordinate cross-functional teams, mentor engineers, liaise with clients, and ensure timely Agile delivery.

As an AI Benchmark & Dataset Engineering intern, you will support benchmarking processes for model evaluation, curate benchmarks, run and validate tests, maintain documentation, and contribute to demos based on outcomes.

Design and execute benchmarks, curate datasets, evaluate benchmarks, and build evaluation infrastructure for AI model assessment while collaborating with R&D.

The role involves designing and scaling MLOps infrastructure on AWS for autonomous driving systems, requiring expertise in machine learning and cloud architecture.

Lead and mentor a team of Applied AI Engineers, oversee AI project lifecycles, and collaborate cross-functionally to enhance AI solutions and customer engagements.

Evaluate AI-generated coding interactions, assess quality of engineering judgments, provide feedback, and define standards for AI coding agents. Comfortable making subjective judgments about engineering quality.

The Principal AI Engineer will architect reusable AI platform services, lead technical design reviews, ensure system reliability, and collaborate across teams to enhance AI frameworks and workflows.