Top Machine Learning Jobs in Romania

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Develops and integrates radiating components and passive radiation-pattern structures for multiband base-station antennas. Responsibilities include 3D and 2D electromagnetic simulation, prototype RF measurements, performance optimization, troubleshooting, technical documentation, and supporting the product lifecycle through mass production. The role collaborates with international cross-functional teams and requires relocation to Rosenheim, Germany.

Leads client application and technology strategy reviews, evaluates AI and emerging technology adoption, assesses architectures, infrastructure, data flows, security, and development practices, and recommends improvements. The role engages senior stakeholders, produces technical guidance, supports sales proposals, and promotes value-added services. Candidates need architecture experience, cloud software delivery expertise, programming and technology breadth, hands-on generative AI integration experience, Agile knowledge, and strong communication and collaboration skills.

Build Solidgate’s ML/AI function from the ground up, developing real-time payment routing models and production pipelines. Own the full ML lifecycle from experimentation and A/B testing through deployment, while solving modeling challenges involving large-scale transactional data. Partner with Product, Engineering, and Data Platform teams to deliver measurable improvements in conversion, approval rates, losses, and revenue.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Lead and grow the Money & Risk engineering team responsible for fraud prevention, risk scoring, reconciliation, settlement, and chargebacks. Own technical direction for scalable, auditable, secure payment systems across 80+ countries. Drive architecture, engineering standards, AI-assisted development, hiring, coaching, and cross-functional delivery with Product, Data, Finance, Compliance, and Operations.

Develops and deploys machine learning and AI solutions across the full data science lifecycle. Responsibilities include exploratory data analysis, model experimentation, scalable data pipelines, evaluation methodology, visualization, deployment monitoring, and custom algorithm development. The role collaborates with engineering, product, and business stakeholders to solve product problems and communicate analytical findings. Preferred experience includes MLOps, A/B testing, causal inference, unstructured data, cloud platforms, real-time processing, and advanced machine learning techniques.

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 the lifecycle and business impact of machine-learning products supporting pricing, matching, ranking, forecasting, fraud, credit risk, and personalization. Lead data-driven discovery, problem framing, metrics, experimentation, roadmap decisions, production monitoring, and model retirement. Partner with data scientists, ML engineers, data engineers, and cross-functional stakeholders across multiple markets. Mentor peers and promote rigorous, transparent data-driven decision-making.

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.

Own the Mapping & Content product domain end to end, including strategy, prioritization, economics, and outcomes. Lead efforts to match hotel rooms and content across suppliers, languages, and formats with high accuracy and coverage. Partner closely with engineering and data science teams on entity resolution, search, matching, catalog, identity, recommendation, and content-quality problems. Identify high-value failures, make trade-offs between precision and coverage, and help build Nuitee’s product organization in a fast-moving environment.

Lead research on interactive avatar models and real-time generative AI video agents. Set research direction, advance multimodal perception and reaction generation, post-train diffusion models, build evaluation frameworks, define datasets, and guide research-to-production efforts. Mentor researchers and influence technical decisions across research, data, and engineering teams.

Develop machine learning and analytics solutions using large-scale datasets. Responsibilities include feature engineering, predictive modeling, workflow optimization, customer implementations, production deployments, stakeholder collaboration, technical training, and resolving data-related challenges. The role requires Python, PySpark, machine learning pipelines, MLflow or Airflow, statistical modeling, and strong communication skills, with preferred experience in fraud detection, risk analytics, compliance, cloud environments, and model monitoring.

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.

Leads the design, development, deployment, and evaluation of machine learning, forecasting, recommendation, and analytical solutions. Partners with Product, Commercial, Engineering, and Data teams to solve business problems, define metrics, build scalable pipelines, and deliver measurable impact. Provides technical leadership and mentorship, promotes model development and governance best practices, and contributes to the data science roadmap.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

Own the Vault product roadmap and lifecycle for a sports media asset platform. Lead discovery with customers, define solutions for content ingest, storage, integrations, AI enrichment, metadata, search, rights, and asset monetization. Partner closely with engineering, sales, and customer success, attend customer events, and prioritize product bets that help sports organizations maximize the value of extensive media archives.

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.

Lead data strategy, architecture, governance, quality, compliance, and technology adoption. Translate stakeholder needs into scalable data initiatives, support advanced analytics and AI/ML, promote data literacy, and guide cross-functional teams toward data-driven decision-making.

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.

Develop AI-augmented blockchain risk and threat intelligence systems, including autonomous monitoring and investigation workflows. Lead forensic investigations into crypto theft, fraud, and market manipulation. Research post-quantum cryptography and quantum threats to blockchain security, evaluate migration paths, and collaborate with internal teams, external stakeholders, and academic institutions to deploy emerging security tools.

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.

Supports Account Executives throughout the sales cycle by leading technical discovery, solution design, demonstrations, workshops, proofs of concept, evaluations, and technical validation. Translates engineering, simulation, HPC, AI, and infrastructure requirements into Rescale recommendations, documents opportunities in Salesforce, resolves technical risks, supports negotiations, and ensures effective post-sale handoffs across Northern Europe.