Top Machine Learning Jobs in Hungary

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Design and own learner models powering mastery and progression features. Build datasets, training and scoring pipelines, evaluation strategies, testing, versioning, and production monitoring for models handling sparse, noisy, evolving behavioral data. Partner with learning scientists, product, and infrastructure teams to define targets, assess calibration, uncertainty, fairness, stability, and interpretability, and clearly communicate model behavior and limitations.

As an AI Solutions Architect, you will design scalable AI architectures, evaluate technical feasibility, define product requirements, provide mentorship, and assess AI models for enterprise deployment.

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.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

The role focuses on creating and implementing Data & AI strategies, advising executives, and managing projects to drive AI adoption and business growth.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.

Lead and grow a machine learning engineering team responsible for fraud and risk models. Manage experimentation portfolios, balance research with delivery, evaluate model performance rigorously, and guide work through production release. Partner with Risk, platform, and infrastructure teams to set priorities, improve training and experimentation systems, communicate results, and make autonomous roadmap decisions. The role is a hands-on player-coach requiring technical depth, people leadership, strong communication, and comfort operating under uncertainty.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

The Staff Software Engineer will lead technical design and delivery for ABBYY Vantage platform, mentor engineers, improve platform health, and influence architectural decisions.

Drive product adoption and customer outcomes via 1:1 and 1:many channels. Onboard and support scale customers, manage escalations, monitor health indicators, collaborate cross-functionally, run webinars and calls, prioritize high-volume tasks, and help mitigate churn while identifying expansion opportunities.

Design, build, deploy, and own production-grade machine learning systems for a proactive AI platform. Responsibilities span data curation, model training, evaluation, inference, monitoring, troubleshooting, optimization, and continuous iteration at scale. The role translates research into reliable products, balances latency, cost, safety, and performance, and collaborates with research, engineering, and product teams. It also includes mentoring engineers, conducting code reviews, and independently driving complex initiatives from concept through launch.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Build and operate production infrastructure for AI services, including CI/CD pipelines, infrastructure as code, model serving, batch scoring, orchestration, monitoring, alerting, debugging, performance, and cost optimization. Partner with data scientists and applied AI engineers to productionize prototypes, design new services, and create reusable infrastructure templates. The role requires onsite collaboration Tuesdays and Wednesdays, with Thursdays strongly encouraged.

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.

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