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.

Lead the LLM team in architecting, deploying, and managing AI models that enhance user interactions. Involve in coding, mentoring, and improving moderation systems for an AI companion platform.

The Pricing Actuary will develop pricing tools, support account pricing, conduct analyses, and contribute to regulatory tasks while collaborating with underwriters.

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

The AI/ML Engineer will develop and implement ML systems for secure-code understanding, manage infrastructure for LLMs, optimize models, and conduct research.

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.

The Senior Data Analytics Engineer will design and maintain analytical pipelines, write SQL for large datasets, utilize AI tools, and communicate insights to stakeholders.

The Senior Data Scientist drives AI platform development, establishes standards, partners with engineering teams, mentors, and ensures responsible AI adoption across Awin.

The role involves building, training, and deploying machine learning models using AWS tools, optimizing workloads, and collaborating with data engineers.

The Machine Learning Engineer develops and deploys NLP and generative AI models, integrating them with business applications and ensuring code quality.

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.

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.

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

The role involves developing AI solutions, managing the lifecycle of models, and collaborating with teams on computer vision projects. Candidates should have experience in AI and software engineering, with strong problem-solving skills.

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.

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

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

As a Machine Learning Engineer, you will develop ML solutions to enhance online gaming features, ensuring high code quality and collaboration with teams.

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.

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.