Top Machine Learning Jobs in Romania

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

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.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

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.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Build and operate ML platform systems for training, evaluation, deployment, and serving. Improve reliability, scalability, cost-efficiency, observability, and automation for GPU and cloud workloads. Develop internal tools and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and developer experience.

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.

Lead large-scale ML projects overseeing their entire lifecycle. Influence technical decisions, design ML pipelines, and mentor other engineers.

The role involves building scalable ML models, mentoring engineers, translating business requirements into specs, and ensuring solutions meet performance constraints.

Develop and implement 3D geometric ML and classical geometry algorithms to analyze and generate 3D intraoral scans and teeth. Build data pipelines, train/adapt 3D models, optimize test-time performance, validate robustness, and collaborate with clinicians and product teams to ship applied research into products.

Design and implement production-grade machine learning components for a next-generation bidder engine in AdTech. Collaborate with software engineers and architects to integrate ML into real-time bidding systems, replace rule-based logic with ML-driven decisioning, optimize performance, support deployment and monitoring, participate in architecture and design reviews, and mentor peers while driving operational excellence.

Join the MLOps team to deploy, monitor and optimize machine learning models in production. Build data and model pipelines, implement CI/CD, ensure model performance and scalability, and collaborate with cross-functional teams to improve infrastructure and delivery.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

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, develop, fine-tune, and deploy ML and NLP models (including MT and LLM finetuning). Own projects end-to-end using Python, TensorFlow/PyTorch, Docker, MLflow, and AWS; perform statistical analysis, monitor production models, document experiments, and collaborate across teams.

The Senior Data Engineer designs, maintains, and optimizes ML applications and data pipelines, ensuring compliance and quality while mentoring junior engineers.

As a Senior Data Engineer, you will design and maintain ML applications, manage workflows, build data pipelines, and mentor junior engineers within a global team.