Top Machine Learning Jobs in Budapest

The Account Executive will drive new business by selling SaaS solutions to Managed Service Providers in the Nordics, managing the full sales cycle, and achieving revenue targets.

The AI Solution Architect will design AI and ML solutions, oversee pre-sales processes, create project estimates, and validate proposals, ensuring technical accuracy and client engagement.

Manage IT projects, oversee project deliverables, mitigate risks, ensure stakeholder satisfaction, lead agile development, and mentor team members.

The Senior Machine Learning Engineer will develop and optimize multimodal understanding models, focusing on computer vision, while leading technical projects and driving architectural decisions for model development.

Lead the design and implementation of Energy Management Systems, align strategies for energy optimization, and ensure integration with digital platforms.

As a Backend Engineer, you'll lead technical problem-solving in data engineering and machine learning, improving risk assessment tools and features.

Lead AI and Data strategies for banking solutions, collaborate with C-level executives, drive product vision, and oversee project execution.

Responsible for project delivery, managing a team, improving solutions, handling customer relationships, and ensuring project alignment with business needs.

The AI Senior Product Manager will lead AI product strategy, manage the AI roadmap, and collaborate with cross-functional teams to deliver innovative solutions.

Lead the modernization of C++ Computer Vision library, design algorithms for Intelligent Document Processing, and mentor engineers.

The job involves data collection and cleaning from various sources, designing and implementing machine learning models, managing workflows, and deploying models on cloud platforms, all within an Agile environment.

Lead initiatives for ERP digital transformation, collaborating with stakeholders, managing projects, and leveraging emerging technologies like AI to enhance workflows.

The Senior Data Scientist will lead projects, provide consultancy in data science and AI, develop internal solutions, and mentor team members while ensuring customer success.

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

The Head of Data will define and implement the data strategy, build infrastructure, and lead a team to drive data-informed decisions and growth across the company.

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

As a Senior AI Engineer, you'll architect and deploy AI solutions on GCP, transform AI experiments to production, and conduct training sessions.

The Senior Solutions Architect (AI) designs solutions, engages with customers, supports sales efforts, and bridges the gap between sales and delivery within AI and data science projects.

The Refining and Petrochemical Process Optimization Engineer analyzes process data, works on AI-driven optimization models, and collaborates with clients to improve operations and drive efficiency.

Lead AI/ML architecture discussions and implement large-scale AI solutions. Mentor engineers while influencing technical standards and championing MLOps best practices.

Design, develop, and deploy machine learning models, taking ownership of ML workflows while collaborating with teams to ensure scalable solutions.

Design and develop a platform for AI and machine learning at Cloudera. Collaborate with various teams and ensure user-friendly interfaces while enhancing developer efficiency.

As a Machine Learning Engineer II at Signifyd, you'll research fraud patterns, build ML models, write code in Python and PySpark, and work with data pipelines.

Lead and manage a team of engineers on the Computer Vision team, overseeing development, deployment, and strategic evolution of AI-powered safety systems.

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.