Top Machine Learning Jobs in Budapest

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

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

Lead and mentor teams on machine learning projects, focusing on computer vision and systems architecture. Drive technical decisions and optimize existing models for performance and reliability.

Lead the development of a strategic analytics roadmap for R&D Procurement, translating business needs into data strategies and fostering team excellence.

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 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.

Drive new business for Orca AI by prospecting and qualifying leads via calls, email, and social media. Meet opportunity quotas, map target accounts, develop outreach templates, nurture relationships, qualify inbound leads, and collaborate with Sales to generate opportunities in global mid/large/enterprise markets.

Lead and grow multiple small, high-output engineering teams focused on AI-powered vulnerability management. Stay hands-on with architecture and code reviews, drive hiring and career development, ensure technical excellence, and coordinate cross-team priorities to deliver customer and revenue impact.

Support ~25–30 learners in a virtual ML/data program by answering questions, facilitating breakout rooms, holding office hours, grading submissions, monitoring Slack, reviewing materials, and providing constructive feedback. Provide bilingual (German/English) technical support focused on data annotation, labeling QA, and generative AI concepts. Participate in onboarding, weekly staff meetings, and submit program improvement write-ups.

Lead legal counsel for an adtech company providing commercial, product, privacy, and regulatory counsel. Negotiate complex agreements, advise on global privacy and IAB standards, support product launches and AI use cases, manage data governance and incident response, oversee corporate compliance and outside counsel, and build scalable legal operations and contract workflows.

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.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

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

The Customer Success Manager will onboard and support Portuguese utility accounts, ensuring effective use of digital twin technology and driving long-term partnerships and customer satisfaction.

The Customer Value Architect will engage with customers to ensure the effective adoption and value realization of the Neara platform, focusing on ROI metrics and customer relationships.

The Machine Learning Engineer II builds production ML models to detect fraud, collaborates on engineering teams, and improves the commerce protection platform.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

Design, build and scale an automated translation-quality evaluation framework for localisation and dubbing. Implement production ML evaluation infrastructure, roll it out across teams, align metrics with product signals, and maintain/iterate systems in collaboration with R&D and cross-functional partners.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

Design, prototype, and productionize specialised LLMs by working with model weights, attention mechanisms, and efficient architectures. Build reusable Python libraries, run experiments and evaluations, and deploy models on GPU-accelerated AWS infrastructure (SageMaker/EC2/S3). Contribute to research-to-production lifecycle, maintain evaluation frameworks, optimize infrastructure for cost and velocity, and collaborate on the team's research agenda while mentoring junior engineers.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.