Top Machine Learning Jobs in Bucharest

The role involves reviewing AI-generated outputs, analyzing multimedia, and providing culturally relevant content feedback. Strong analytical skills and attention to detail are required.

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

As a Senior Product Manager, you will define product vision, analyze market trends, manage product KPIs, and collaborate with teams to deliver innovative solutions.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

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.

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.

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.

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

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Convert data-science prototypes into production-grade ML systems: design, develop, test, deploy, maintain and optimize scalable solutions on-premises or in cloud environments, improve DS/ML tooling and share best practices across teams.

Develop, deploy, and maintain machine learning models for operational defence and security systems. Work with data science teams to produce production-grade models using Python, SQL and ML frameworks, apply model deployment and MLOps practices, and integrate models into mission-critical systems.

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

Design, back-test and implement pricing, risk management, and agency trading strategies; collaborate with developers to integrate models into the trading platform; participate in product ideation, modeling, parameter tuning, and risk evaluation.

Own post-sale customer lifecycle for Portuguese utility accounts: onboard, embed Neara into workflows (vegetation management, clearance analysis, CAPEX), run QBRs, monitor health scores, drive renewals and upsell, and align product roadmap with local market needs.

Architect and build core Decision Intelligence platform capabilities: heterogeneous data integration, semantic modeling, simulation and optimization services, scalable distributed backend systems, AI/ML and LLM-powered production deployments, data pipelines, and operational tooling. Provide technical leadership, own full lifecycle from design to production, improve reliability, observability, and drive engineering excellence.

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 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.

Design, prototype, and deploy ML models for personalization in mobile games. Implement and evaluate algorithms (including RL and generative methods), run A/B tests, convert prototypes to production via MLOps, monitor player data, and collaborate with product, engineering, and analytics teams to improve retention and monetization.

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.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

Design, build, and productionize scalable ML solutions and platforms: convert research code to reusable Python packages, implement experiment tracking and versioning, create distributed training and deployment pipelines, enable monitoring and automated retraining, enforce ML lifecycle governance, and collaborate with cross-functional teams to deliver secure, production-ready ML systems.

Lead design and productionization of ML pipelines, evaluation systems, and AI agents for cybersecurity. Own experimentation-to-production lifecycles, integrate ML across product teams, scale ML infrastructure, optimize agent performance, and mentor junior engineers while collaborating with CTO and product leadership.