Top Machine Learning Jobs in Hungary

Build and maintain scalable backend systems that integrate real-time data, machine learning, and optimization models to optimize renewable and battery storage operations. Own architecture through deployment and maintenance, collaborate with engineers and data scientists, troubleshoot performance issues, and improve monitoring, reliability, and system performance.

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.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

The AI / Machine Learning Researcher will conduct applied research, develop prototypes, and work with data to create AI-driven features related to home design.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

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.

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.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Design, train, and improve computer-vision and generative ML models; handle end-to-end ML pipeline from data collection to deployment; prototype research ideas and convert successful prototypes into product-ready solutions; optimize models for efficient on-device inference and collaborate with product teams to integrate models.

As a Senior Python Engineer, you'll design and implement scalable solutions, collaborate across teams, and manage infrastructure on cloud platforms, ensuring high-quality deliverables.