Top Machine Learning Jobs in Hungary

Join the Fraud Handling team to automate fraud investigations, improve tooling, and optimize workflows, focusing on customer experience.

The Account Executive will drive new business by selling SaaS solutions to Managed Service Providers in the Nordics, managing the full sales cycle, and achieving revenue targets.

The Data Engineer will design and manage data workflows, develop AI-driven solutions, and maintain Snowflake data pipelines, improving analytics and reporting processes.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead technical strategy and build scalable AI/ML platform infrastructure on AWS and Kubernetes. Design IaC, CI/CD, observability, security, and cost/capacity practices. Mentor engineers, improve deployment reliability, and enable generative AI workloads, model routing, and operational metrics across multiple environments and regions.

Combine and prepare data from multiple sources, clean and validate datasets, perform analysis and exploratory data work, and build/test machine learning models in Python. Translate business questions into analytics tasks, present insights to technical and non-technical stakeholders, and collaborate across teams to support reporting and decision-making.

Lead the execution and ongoing evolution of HR's GenAI testing and release-readiness approach, ensuring AI capabilities perform reliably and meet oversight standards.

Lead a team of ML Engineers to develop and optimize ML systems, ensuring high-quality clinical care through AI. Foster collaboration across teams for innovative AI/ML features.

Lead the Weaviate Database product: own roadmap and strategy, translate customer and research insights into features across schema, storage, replication, and search quality, coordinate cross-functional teams, drive developer adoption, and measure feature outcomes post-release.

Lead design and management of taxonomies for Non-Financial Risk, collaborating with stakeholders to develop categories supporting semantic search, knowledge graphs, recommendation engines and ML/NLP capabilities. Implement taxonomy governance and change management, perform concept/entity extraction and metadata modeling, and support regulatory and financial data requirements across the Legal & Compliance function.

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.

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.

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.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Graduate R&D training program developing talent for coatings innovation across product formulation, simulation (CFD/DEM/FEA, Aspen) and digital science (big data, ML, digital twins). Candidates will support materials and process development, modeling, and AI-driven data platforms. Requires research output, strong problem-solving, communication, and willingness to travel.

The Senior AI & Data Consultant will design AI strategies, advise executives, oversee AI tool adoption, and help grow AI services, primarily within the banking sector.

As an Engagement Manager, you'll ensure customer success in protein R&D by building relationships, tracking metrics, and leading cross-functional teams. You'll manage enterprise accounts and advocate for customers while aligning their needs with Cradle's offerings.

Lead horizon scanning and external intelligence to identify AI and disruptive tech opportunities for R&D. Translate trends into actionable briefs, design and run pilots with stage-gates, and scale successful capabilities into production. Influence senior leadership with strategic foresight, build external partnerships, and embed a future-back innovation culture across cross-functional teams to measurably improve R&D outcomes.

Lead the estimating product roadmap for an AI-powered construction SaaS: prioritize and ship MVPs, run rapid experiments, conduct user discovery with estimators, define success metrics, and iterate using data and user feedback.

Lead development, validation and governance of GLMs across European markets; translate pricing models into portfolio steering, monitoring, dashboards and executive insights; act as AKUR8 technical authority; embed pricing governance and mentor pricing leads while collaborating with actuarial, data, underwriting and business stakeholders.

Lead development and productionization of TTS and voice-generation systems: train, fine-tune, evaluate models, deploy and optimize for inference, latency, scaling, monitoring, and reliability; translate research into production and define technical direction while collaborating with research, engineering, and product.

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.