Top Machine Learning Jobs in Budapest

Build and operate scam prevention and detection systems protecting Wise customers from financial crime. The role is approximately 80% backend and 20% frontend, involving scalable web applications, real-time controls, transaction monitoring, automation, databases, CI/CD, distributed systems, and product-focused project ownership. Collaboration with Product, Data Science, and Investigations teams is required, with machine learning lifecycle experience considered beneficial.

Build and operate low-latency backend systems for Wise’s North American fraud and risk prevention platform. Design real-time decisioning infrastructure, optimize databases and distributed event processing, integrate machine learning models, and maintain reliable systems at billions of transactions. Collaborate with software engineers, data scientists, and analysts to refine fraud rules, reduce false positives, support Instant ACH, and expand capabilities into sanctions screening and AML.

Lead AML search-quality modeling and experimentation, improving fuzzy name matching across sanctions, PEP, and adverse media data. Build evaluation datasets and metrics, deploy and monitor production models, manage drift and incidents, investigate customer matching issues, and balance accuracy with performance and cost. Collaborate cross-functionally with Product, Engineering, QA, DevOps, and Customer Success while mentoring teammates and advancing data science practices.

Owns account-based marketing for enterprise accounts, Google Cloud co-marketing, sales enablement materials, and targeted events across EMEA and the Nordics. The role partners with sales leaders, technical teams, Google partner teams, customers, and contractors to generate senior meetings and opportunities, secure co-marketing funding, improve sales kit usage, and support revenue growth.

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.

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.

Own the architecture and production delivery of LLM, RAG, agentic, and classical ML 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.

Advise city governments across the Americas on designing, prototyping, and launching digital services. Guide system and data architecture, DevOps, infrastructure, AI and machine learning decisions, rapid prototyping, and end-to-end product delivery. Work hands-on with multidisciplinary teams while coaching clients and influencing technical decisions without direct authority. The role requires senior technical leadership, strong communication, and professional English plus Spanish and/or Portuguese.

Lead and develop machine learning, LLM, and automation projects using large-scale network and device telemetry. Clean and transform data, engineer features, build models for anomaly detection, forecasting, and optimization, and deliver reproducible analyses with Python and SQL. Collaborate with engineering and product teams to deploy, integrate, and monitor models in production while contributing to MLOps workflows.

Owns software architecture and computer vision direction for Hawk-Eye’s tracking and automated officiating systems. Leads camera calibration, ball and skeletal tracking, ML integration, and R&D across sports and deployment environments. Evaluates modern AI development tooling, oversees ML training and synthetic data infrastructure, mentors technical leaders, supports hiring, and serves as an escalation point for high-risk live delivery issues. The role is an individual contributor focused on technical authority rather than people, project, or product management.

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.

Own the vision, roadmap, commercialization, and executive strategy for Kaltura’s AI-powered learning and enablement platform. Lead 0-to-1 MVP delivery and post-MVP expansion across AI course generation, conversational tutoring, personalized learning, simulations, and enterprise foundations. Integrate acquired products and agentic AI capabilities while partnering with engineering and GTM teams on architecture, pricing, packaging, positioning, and enterprise sales. The role requires deep B2B SaaS product leadership, enterprise learning platform experience, production AI/ML delivery, and strong technical and commercial judgment.

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.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Own the strategy and execution of integrated B2B marketing campaigns for enterprise AI solutions and a self-service platform. Drive pipeline, product adoption, acquisition, and category awareness through ABM, content, events, paid media, lifecycle, social, partnerships, PR, and sales activation. Coordinate internal teams and agencies, optimize messaging and conversion paths, and measure performance across account engagement, pipeline, product adoption, and awareness.

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.

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.

Design and deliver internal systems, Go services, APIs, data models, dashboards, visualizations, and LLM-powered workflow features. The role includes writing specifications, estimating work, code reviews, testing, documentation, security, performance, accessibility, and collaboration with nontechnical stakeholders. The engineer will handle sensitive people data, mentor colleagues, and improve team practices in a globally distributed environment.

Own the anti-fraud product roadmap across signup, payments, account protection, compute-abuse prevention, risk scoring, manual review, customer verification, and appeals. Partner with Trust & Safety, Engineering, Data Science, Analytics, and Legal to define detection rules, models, incident responses, experiments, and compliance processes. Monitor fraud losses, chargebacks, false positives, review throughput, and detection speed while minimizing customer friction.

Lead and develop Customer Success Managers across Southern Europe, especially France and Spain. Ensure enterprise customers successfully launch and derive measurable value from the product, build executive and technical stakeholder relationships, translate product capabilities and performance data into business outcomes, establish scalable customer success processes, and handle complex escalations.

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.

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.

Lead product marketing for the French market by developing localized positioning, messaging, sales enablement, launch campaigns, and customer insights. Partner cross-functionally with product, sales, marketing, engineering, design, communications, and partnerships teams. Track adoption and market performance, identify growth opportunities, and lead product marketing strategy for a major Paris customer event. The role requires French and English fluency, B2B technology marketing experience, and travel within France and internationally.