Top Machine Learning Jobs in Romania

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.

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 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.

Design and implement backend services and APIs in Go, collaborate with cross-functional teams, maintain platform stability and scalability, communicate proactively, and contribute to team planning and continuous improvement.

The Data Engineer role focuses on developing predictive models and enhancing data engineering practices to support business objectives. Responsibilities include writing clean code, model evaluation, and collaboration with a global team to improve reporting and campaign management.

Lead AI initiatives, craft product vision, manage lifecycle, utilize data for decisions, and foster collaboration across teams.

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.

Lead the deployment and management of ML/DL models, design systems for dataset management, and optimize model serving processes in production.

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.

The role involves R&D of ISP technology, algorithm development, HW/SW co-design, sensor tuning, and customer support for image processing solutions.

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

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.

Act as a senior ML advisor mentoring engineering teams: review BigQuery ML implementations, guide model selection, training, validation, evaluation, and deployment; teach ML fundamentals and metrics; recommend governance, standards, and operational improvements; provide on-demand technical consultations to raise ML maturity across multiple teams.

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.

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.

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 operate ML platform systems for training, evaluation, deployment, and serving of generative models. Improve reliability, scalability, performance, observability, and automation for GPU and cloud workloads. Develop internal tooling and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and operational improvements.

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.

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.

Lead applied ML efforts to embed LLMs and agentic workflows into production, focusing on anti-fraud, automated evaluation, and operational automation. Design reusable architectures, implement observability and A/B tests, partner with delivery and clients, and set engineering standards for prompts, agents, pipelines, and CI/CD.

Design, build, and operate scalable GCP cloud infrastructure for AI/ML platforms using Terraform and IaC. Manage multi-project environments, security, governance, cost control, hybrid networking, GPU provisioning, and self-hosted autoscaling CI/CD runners. Integrate infrastructure tooling into CI/CD pipelines and take end-to-end ownership of delivered solutions while collaborating with stakeholders.

Design, build, and operate an ML platform on GCP (Vertex AI). Develop and automate ML pipelines, provision infra with Terraform, create CI/CD workflows with GitHub Actions, build production Python APIs, implement model monitoring and observability, and support GPU-based training in HPC/Slurm environments.