Top Machine Learning Jobs in Romania

Develop automotive safety and sensing systems using C/C++ and Python. Responsibilities include algorithm requirements engineering, concept design and documentation, target-platform implementation, simulation-based testing, product customization, and compliance with Magna’s quality strategy. The role requires advanced programming, mathematics, and algorithmic skills, plus knowledge of object-oriented programming, multithreading, real-time systems, automated driving architecture, and basic machine learning.

Conduct AI and machine learning research for cybersecurity applications, including training LLMs, developing graph neural networks, creating predictive and synthetic-data models, and improving threat detection. Collaborate with senior data scientists, engineers, and product managers; analyze large datasets; automate and visualize AI development workflows; and use Python to preprocess data, generate insights, and train models. This is a six-month fixed-term position for students or recent graduates.

Develop automotive safety algorithms for vehicle exterior monitoring. Responsibilities include understanding use cases, collecting and preparing data, handling edge cases, supporting design and testing strategies, participating in Agile software development, and building machine learning models for automotive applications under experienced engineers’ guidance.

Provide second-level operations support for core and telco cloud infrastructure: manage incidents, preventive maintenance, ticket lifecycle, performance and lifecycle management, automation scripting, procedures, team coordination and training. Role includes project support, reporting insights, and a six-month deployment to Saudi Arabia.

Engineer automation, tooling, and detections to improve incident response operations. Automate workflows, build detections and integrations, develop cloud-native tooling, and collaborate with threat intelligence to speed investigations and reduce false positives.

Perform malware and threat research across the attack lifecycle, validate and improve Falcon detections, collaborate with engineering and ML teams, automate analysis, support external security testing, and translate findings into product protections.

Own end-to-end talent acquisition and headhunting for Arago’s technical hiring across France, North America, and Israel. Partner with the CEO, CTOs, and engineering leaders to define roles, source and close senior engineers, scientists, and technical leaders across hardware, optical, software, and research. Build proactive sourcing pipelines, improve interview processes and hiring standards, monitor pipeline health and hiring velocity, and communicate progress to leadership.

Develop and industrialize statistical, predictive, machine learning, and GenAI models for insurance and reinsurance use cases. Collaborate with clients, underwriters, actuaries, and cross-functional teams to deliver AI projects involving NLP, OCR, visualization, and process transformation. Conduct applied research, validate model robustness, improve interpretability, communicate findings, document work, share knowledge, and comply with information security, GDPR, and other data protection requirements.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Leads end-to-end delivery of IT, AI, GenAI, data analytics, data engineering, and digital transformation projects. Manages scope, budgets, resources, risks, testing, quality, customer communications, technical delivery, and transition to operations. Coordinates multidisciplinary teams and senior stakeholders across complex enterprise environments, ensuring timely delivery and alignment with business requirements. Oversees project planning, governance, documentation, performance reporting, continuous improvement, and employee development.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Designs enterprise-grade cloud, data, and AI solutions for airline operations. Responsibilities include assessing technology landscapes, identifying modernization and integration opportunities, translating business priorities into technical architectures, creating solution blueprints and documentation, promoting enterprise architecture best practices, and guiding customer teams through transformation initiatives.

Design and lead multi-domain cloud, data, and AI architectures for aviation operations. Translate business needs into scalable technical solutions, guide technology decisions, oversee platform integration, and provide technical leadership and mentorship to customer development teams. Collaborate with technical and business stakeholders while balancing performance, security, scalability, and cost.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Develop and prototype computer-vision and ML features: design experiments, collect/annotate/clean data, build/train/evaluate/optimize models, implement and optimize vision/signal-processing algorithms, and support product development and PoC delivery.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

Lead product strategy and roadmap for Memory, an agentic AI intelligence engine. Drive cross-functional delivery, commercial alignment, enterprise readiness, and measurable adoption, retention, and revenue outcomes while mentoring junior product team members.

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.

Design, build, deploy, and maintain ML/DL models and AI agents; productionize prototypes, integrate AI into enterprise systems, provide 3rd-level support, follow ITSM processes, and ensure ethical and regulatory compliance while collaborating with cross-functional stakeholders.

Lead and build Solidgate's ML/AI function from scratch: define vision and architecture, hire and lead the team, deliver real-time decisioning and recommendation systems, partner with Product/Engineering/Data Platform, run experiments and A/B tests, and translate models into revenue-driving production features.

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

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