Top Machine Learning Jobs in Hungary

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

The Head of Computer Vision will lead a team of ML Engineers, drive the long-term ML strategy, focus on breakthrough innovations, and establish cross-functional partnerships while mentoring and scaling the team effectively.

Lead ML infrastructure upgrades in hyperscale data centres across EMEA: manage contractors, schedules, logistics, mechanical/electrical/fibre coordination, commissioning (L3/L4 support), risk mitigation, reporting, and lessons learned throughout full project lifecycle.

The Machine Learning Engineer II builds production ML models to detect fraud, collaborates on engineering teams, and improves the commerce protection platform.

The Junior ML Engineer will automate information extraction, build models, develop ML pipelines, and analyze data for insights.

The Office Coordinator handles administrative duties like organizing meetings, managing supplies, facilitating communication, and supporting office operations, ensuring efficiency.

Responsible for project delivery within an IT solution area, managing teams, ensuring customer satisfaction, and improving team dynamics.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Design, prototype, and productionize specialised LLMs by working with model weights, attention mechanisms, and efficient architectures. Build reusable Python libraries, run experiments and evaluations, and deploy models on GPU-accelerated AWS infrastructure (SageMaker/EC2/S3). Contribute to research-to-production lifecycle, maintain evaluation frameworks, optimize infrastructure for cost and velocity, and collaborate on the team's research agenda while mentoring junior engineers.

Lead end-to-end ML systems for LLM/NLP document intelligence and supply-chain predictive analytics. Design and fine-tune transformer-based models, implement RAG pipelines, build forecasting, classification and optimization algorithms, and productionize models using containerization and MLOps tools. Ensure robust, explainable models deployed to secure on-premise environments with monitoring and continuous improvement.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Lead and hire an applied research team to develop multimodal, spatial, and graph-based models for the "Intelligent Canvas." Define research roadmap, translate research into product, pioneer methods for spatial collaboration datasets, build evaluation and governance processes, and partner with engineering and product to ship impactful ML features.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead and manage a team of ML engineers and researchers to design, deploy, and monitor scalable computer vision, graphics, and generative AI models for a VFX platform. Define ML strategy, collaborate with product and engineering to integrate models into production pipelines, ensure responsible AI practices, and partner with infrastructure teams to build efficient training and deployment systems.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

Design, build and scale an automated translation-quality evaluation framework for localisation and dubbing. Implement production ML evaluation infrastructure, roll it out across teams, align metrics with product signals, and maintain/iterate systems in collaboration with R&D and cross-functional partners.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Build and own production ML and LLM systems for onboarding, risk, compliance, and operations. Design models, engineer features from messy data, balance performance and interpretability, integrate with backend training/inference pipelines, write tested code, and partner with product and operations to drive scalable, reliable automation.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

The AI / Machine Learning Researcher will conduct applied research, develop prototypes, and work with data to create AI-driven features related to home design.

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.