Top Machine Learning Jobs in Hungary

Support the development and operation of cloud infrastructure, data solutions, internal AI applications, APIs, and containerized applications. Contribute to database and data engineering tasks, software integration, DevOps, and AI platform initiatives while learning from experienced colleagues. Collaborate across teams to create reliable tools that improve productivity for Ericsson’s Microwave organization.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Build and improve scam prevention infrastructure, including backend and frontend detection systems, financial crime controls, and scalable web applications. Own projects end to end, primarily focused on backend development, while operating distributed systems and CI/CD pipelines. Design resilient database schemas, optimize queries, maintain high code quality through test-driven development, and collaborate with Product, Data Science, and Investigations teams to protect customer funds.

Develop quantitative models for sports pricing, odds compilation, and risk management. Analyze large datasets, identify trading insights, deploy models with engineers, monitor performance, conduct back-testing, and continuously improve model accuracy. Collaborate with data scientists and engineers to align models with business needs. The role requires Python, sports betting market knowledge, statistical modeling, and experience with odds and margin optimization.

Develop and deploy machine learning models for retrieval, reranking, indexing, search relevance, crawling, and content understanding. Build large-scale embedding and retrieval systems, evaluation pipelines, and quality metrics for agent-native search. Integrate models into high-throughput production services while balancing latency, quality, and cost. Collaborate on architecture and product decisions involving search, retrieval, and LLM-based systems.

Leads the Tool Test Rack Development team, overseeing test rack solutions, reliability infrastructure, project plans, budgets, milestones, risks, suppliers, compliance, documentation, and technical integration. Provides technical and disciplinary leadership, coordinates global teams, presents progress to management, supports audits and design reviews, and advances test infrastructure, processes, and standards.

Own the end-to-end strategy, roadmap, and outcomes for a network intelligence and security product serving ISP customers. Partner with engineering, data science, security, and compliance teams on architecture, ML evaluation, privacy, and production delivery. Conduct customer discovery, analyze market and threat trends, define requirements and metrics, build business cases, and represent the product to technical customers, executives, sales, marketing, and public audiences.

Owns product finance for the PCS segment across Europe, including financial planning, forecasting, budgeting, reporting, profitability analysis, and business partnering. The role supports capital allocation, go-to-market strategy, product launches, productivity initiatives, and performance optimization. It also drives AI and automation adoption across finance processes, develops actionable insights for senior management, and coordinates with regional and global finance teams to align strategy and execution.

Develop and maintain ABBYY Vantage cloud-native services supporting document AI capabilities, including OCR pipelines, AI skill workflows, APIs, infrastructure, and analytics. Design, test, deploy, and monitor reliable software; participate in code reviews; improve tooling and delivery workflows; resolve production incidents; and collaborate with product, UX, ML, and engineering teams.

Owns monthly and quarterly Equipment P&L closing, financial reporting, KPI tracking, budgeting, forecasting, variance analysis, and scenario modeling. Partners with finance, controllership, commercial, and operations teams to identify risks, recommend corrective actions, and support productivity initiatives. Uses Power BI, Power Query, SQL, Excel, SAP, AI, and automation to streamline reporting and improve financial processes across Europe.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Develop and deploy production-grade AI and machine learning solutions for industrial manufacturing. Own projects from problem definition through operational integration, conduct data-driven optimization, advance ML and LLM-based methods, and collaborate with external partners. Coach employees on AI, establish standards, and advise stakeholders. The role requires Python, Azure, Kubernetes, infrastructure as code, cloud AI architectures, process simulation, optimization, and experience scaling ML systems.

Own the architecture and production delivery of LLM, RAG, agentic, and classical machine learning systems. Design scalable AI solutions on Databricks using Spark and MLflow, optimize latency, cost, reliability, and performance, and implement evaluation, monitoring, CI/CD, rollback, and versioning. Translate client needs into end-to-end architectures, lead technical decisions and pre-sales discussions, and mentor team members on GenAI and ML best practices.

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.

Lead and grow the Money & Risk engineering team responsible for fraud prevention, risk scoring, reconciliation, settlement, and chargebacks. Own technical direction for scalable, auditable, secure payment systems across 80+ countries. Drive architecture, engineering standards, AI-assisted development, hiring, coaching, and cross-functional delivery with Product, Data, Finance, Compliance, and Operations.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

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.

Leads end-to-end testing, regression cycles, and release-readiness assessments for agentic and workflow-driven HR GenAI tools. Coordinates testing across product, engineering, governance, risk, and business stakeholders; maintains test frameworks, evaluation criteria, documentation, defect tracking, and risk indicators. Partners with control teams to ensure GenAI capabilities meet monitoring, oversight, data quality, and operational risk standards as models, prompts, and system architectures evolve.

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.

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.

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.