Top Machine Learning Jobs in Hungary

Build Solidgate’s ML/AI function from the ground up, developing real-time payment routing models and production pipelines. Own the full ML lifecycle from experimentation and A/B testing through deployment, while solving modeling challenges involving large-scale transactional data. Partner with Product, Engineering, and Data Platform teams to deliver measurable improvements in conversion, approval rates, losses, and revenue.

Own the Mapping & Content product domain end to end, including strategy, prioritization, economics, and outcomes. Lead efforts to match hotel rooms and content across suppliers, languages, and formats with high accuracy and coverage. Partner closely with engineering and data science teams on entity resolution, search, matching, catalog, identity, recommendation, and content-quality problems. Identify high-value failures, make trade-offs between precision and coverage, and help build Nuitee’s product organization in a fast-moving environment.

Lead research on interactive avatar models and real-time generative AI video agents. Set research direction, advance multimodal perception and reaction generation, post-train diffusion models, build evaluation frameworks, define datasets, and guide research-to-production efforts. Mentor researchers and influence technical decisions across research, data, and engineering teams.

Data Scientist Intern supporting AI data quality and analytics for autonomous driving. Responsibilities include using agentic AI and data mining tools to improve training datasets, investigating annotation and pipeline issues, developing Python analyses, and collaborating with machine learning engineers to improve training workflows and efficiency.

Supports cross-functional engineering teams on a Level 4 autonomous trucking project. Responsibilities include coordinating engineering processes, analyzing software delivery issues across the machine learning pipeline, improving collaboration and execution, and experimenting with LLM-based tools. The role requires familiarity with software engineering, machine learning, and Agile processes, plus ongoing university studies in Hungary and English fluency.

Develop, validate, deploy, and maintain machine learning solutions for medical device manufacturing and CAD/CAM systems. Build deep learning and multimodal AI models using computer vision, 3D geometry, meshes, images, and structured data. Integrate models into production systems, conduct performance analysis, maintain ML workflows, and translate research into practical manufacturing applications while collaborating with engineering, manufacturing, and product teams.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

Designs and delivers production credit analytics and machine learning platforms for regulated financial-services clients. Responsibilities include architecture, Python and SQL development, reproducible ML workflows, MLOps, data quality, model governance, Snowflake application reliability, monitoring, access control, and technical leadership. The role works directly with clients and modelling specialists to productionize credit-risk models, support multiple portfolios, and evaluate responsible uses of LLM-assisted analytics.

Lead the software engineering team’s daily operations, technical direction, architecture, reliability, planning, delivery, hiring, and leadership development. Partner with Product, Core ML, and Hardware teams to scale cloud services and deployed checkout devices internationally. Improve monitoring, release processes, testing, incident response, and AI-assisted development practices while balancing customer needs and long-term strategy.

Leads AI Engineers, Data Scientists, and software engineers developing, deploying, and operating machine learning and generative AI solutions. Oversees the full model lifecycle, including problem definition, data preparation, experimentation, evaluation, production operation, and MLOps. Manages hiring, performance, coaching, and career development while partnering with stakeholders and presenting technical tradeoffs to leadership. Guides scalable AI platform architecture on AWS and promotes responsible, secure, reliable, and fair AI practices.

Designs, builds, and maintains scalable data and machine learning infrastructure, pipelines, databases, integrations, self-service tools, and MLOps workflows. The role includes platform expansion, observability, security, data quality, performance optimization, infrastructure as code, CI/CD, documentation, and collaboration with data scientists, analysts, and stakeholders. Strong experience with cloud platforms, Kubernetes, containerization, databases, big data, orchestration, and AI-assisted development is required.

Performs business analysis and serves as the IT-side Product Owner for AI/ML and data use cases. Gathers requirements, translates business needs into technical specifications, coordinates PoC and MVP development, supports Data Analytics, Data Science, and Generative AI solution assessments, and facilitates collaboration among business stakeholders, developers, architects, and product owners.

Own the Vault product roadmap and lifecycle for a sports media asset platform. Lead discovery with customers, define solutions for content ingest, storage, integrations, AI enrichment, metadata, search, rights, and asset monetization. Partner closely with engineering, sales, and customer success, attend customer events, and prioritize product bets that help sports organizations maximize the value of extensive media archives.

Corti is seeking passionate individuals to join their talent community, shaping the future of healthcare through AI technologies. Opportunities include roles in AI technology, machine learning, and software development, focusing on reducing errors and improving patient outcomes.

Interns support PPG’s digital transformation and research projects across artificial intelligence, software development, physical rendering, analytical science, and materials science. Responsibilities may include data analysis, model development, web application development, 2D/3D rendering, chemical experiments, instrument operation, laboratory reporting, data integration, and technical research. Interns receive expert mentorship and are expected to collaborate effectively, solve problems, follow safety and quality standards, and commit to at least six months when possible.

Build and maintain internal machine learning platforms, distributed data processing solutions, reusable tooling, and large-scale data pipelines. Improve system reliability, performance, automation, and maintainability while collaborating with data scientists and engineers to productionize machine learning solutions. Contribute to architecture and engineering practices, and mentor teammates.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

One-year R&D trainee program at PPG’s Global Coatings Innovation Institute, featuring rotations, training, project participation, mentoring, and presentations. Trainees may specialize in product formulation, simulation and modeling, digital technology and AI, or color science and 3D rendering. The role supports coatings innovation, data-driven development, digital twins, process optimization, and advanced product development, with potential management, technical, or cross-functional career paths.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Develop AI-augmented blockchain risk and threat intelligence systems, including autonomous monitoring and investigation workflows. Lead forensic investigations into crypto theft, fraud, and market manipulation. Research post-quantum cryptography and quantum threats to blockchain security, evaluate migration paths, and collaborate with internal teams, external stakeholders, and academic institutions to deploy emerging security tools.

Supports Account Executives throughout the sales cycle by leading technical discovery, solution design, demonstrations, workshops, proofs of concept, evaluations, and technical validation. Translates engineering, simulation, HPC, AI, and infrastructure requirements into Rescale recommendations, documents opportunities in Salesforce, resolves technical risks, supports negotiations, and ensures effective post-sale handoffs across Northern Europe.

Manage export sales orders from compliance and documentation through delivery and billing. Responsibilities include export license verification, customs support, order blocking and modification, intercompany automation monitoring, stock allocation, backorder management, logistics coordination, and continuous process improvement. The role requires SAP and Excel expertise, international trade knowledge, order-to-cash experience, strong analytical and problem-solving skills, and fluent English plus German or Dutch.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Own enterprise customer relationships from kickoff through adoption and ongoing value realization. Lead success plans, QBRs, executive conversations, escalations, stakeholder alignment, training, and strategic recommendations. Translate technical capabilities and performance data into business outcomes such as revenue, conversion, and customer experience. Partner with Account Management to support retention and expansion while improving scalable onboarding and support processes.

Lead the strategy, roadmap, and evolution of Stewardship Intelligence, a corporate governance and capital-markets data product. Manage Product Management and Research teams, improve dataset quality and coverage, introduce automation and AI, and partner with Engineering and UX to deliver product experiences and data services. Engage institutional investors, governance professionals, advisers, and issuers while collaborating with Commercial, Marketing, Sales, and Customer Success on positioning, launches, adoption, and customer value.