Top Machine Learning Jobs in Budapest

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.

Design and optimize ML pipelines for healthcare, develop scalable AI solutions, ensure model performance, and collaborate with teams.

Lead development of ML applications with a focus on generative AI and LLM solutions, partnering with teams to improve software products and mentoring junior engineers.

Lead AI-powered analytics capabilities for customer engagement in pharma launches, collaborating with stakeholders to create data-driven solutions.

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.

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

Lead and mentor teams on machine learning projects, focusing on computer vision and systems architecture. Drive technical decisions and optimize existing models for performance and reliability.

Lead Machine Learning Engineering projects using Databricks. Collaborate on AI/ML solutions, engage with clients, and drive innovation.

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.

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

The role involves porting and optimizing C++ applications for ARM architecture, developing ML models, and conducting system validation on NVIDIA Jetson devices.

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

Lead high-scope AI and ML projects, focusing on Generative AI. Collaborate with cross-functional teams to design and implement solutions that enhance patient care.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

N-iX seeks a Data Engineer with MLOps experience to create scalable ML pipelines, deploy models, and contribute to best practices in data engineering and coding.

The Machine Learning Platform Engineer will manage cloud infrastructure, support AI researchers, and deploy models in production, focusing on MLOps and CI/CD practices.

The Senior Quality Operations Engineer will manage quality systems, ensure compliance with regulations, oversee audits, and optimize workflows in medical technology.

As an AI/ML Specialist Solutions Architect, you will design scalable AI solutions, manage AI deployments, and collaborate with clients to enhance customer satisfaction.

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 AI/ML Engineer will design and implement AI/ML solutions, lead project discussions, mentor junior staff, and provide technical guidance in a cloud environment.

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

The Senior ML Engineer will enhance fine-tuning methodologies, research inference optimization techniques, and re-implement LLM architectures focusing on distributed training and efficiency.