Top Machine Learning Jobs in Romania

As a Senior Product Data Analyst, you'll influence product and marketing strategy using data; analyze customer behavior, build dashboards, and partner across teams to drive data-driven decisions.

The Senior ML Platform Engineer will develop and optimize components of the ML Platform, manage large scale ML systems, ensure high performance in AWS and Kubernetes, and address user challenges with scalable solutions.

The Principal Data Product Manager will lead ML and AI initiatives, define strategies, manage product roadmaps, and collaborate with teams to deliver scalable solutions that drive business outcomes.

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 LLM team to architect, train, and deploy AI models, optimizing real-time user experiences and creating advanced moderation systems.

Lead the architecture and development of a programmatic AI-enhanced engineering system, overseeing a team and ensuring software 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 AI/ML Engineer will develop and implement ML systems for secure-code understanding, manage infrastructure for LLMs, optimize models, and conduct research.

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

The role involves developing and deploying big data anti-fraud detection solutions using Python and PySpark while collaborating with team members to ensure quality code and production support.

The AI/ML Engineer designs, develops, and deploys machine learning models and AI solutions, collaborating with cross-functional teams to drive automation and advanced analytics while ensuring data quality and model performance.

Lead the design, implementation, and deployment of Generative and Agentic AI solutions, integrate LLMs and databases, and manage cloud infrastructure.

Design, build, and operate large-scale geospatial data pipelines and production ML/agent systems. Own data ingestion, distributed processing, model training, deployment, monitoring, and observability. Develop backend services and tooling, integrate agent frameworks, collaborate with product and customer teams to drive revenue and reliability.

Lead design, build, and deployment of ML solutions (classifiers, regressors, ranking, LLMs). Own ML lifecycle tasks: data exploration, feature engineering, training, evaluation, deployment, monitoring, and pipelines. Collaborate cross-functionally to deliver measurable impact, ensure code quality, reproducibility, and responsible ML practices.

Lead design and delivery of scalable ML solutions across the platform, owning the full ML lifecycle from feature engineering to deployment. Influence roadmap, translate technical findings for stakeholders, enforce best practices, and mentor junior engineers while shipping production-quality ML products.

As an AI/ML Agentic Engineer, you'll design autonomous AI agents, build multi-agent systems, and deploy predictive models while collaborating with stakeholders and advancing AI strategies.

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 Data Engineer will enhance data workflows, collaborate with teams to build datasets, influence data strategies, and implement pipelines for effective data usage in AI/ML products.

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

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

As a Machine Learning Engineer, you will develop and maintain ML pipelines, support Google Cloud Infrastructure, ensure code quality, and conduct trainings. Collaborate with Data Scientists and effectively communicate with stakeholders to bridge data science and engineering.

Develop, optimize, and deploy machine learning and NLP models. Design generative AI solutions and maintain APIs for integration with business applications.

The Senior AI/ML Engineer at Nansen will develop AI models for blockchain data analysis, collaborating with teams to enhance platform capabilities and mentor junior engineers.

The Senior Data Engineer designs, maintains, and optimizes ML applications and data pipelines, ensuring compliance and quality while mentoring junior engineers.