Top Machine Learning Jobs in Bucharest

Own an internal platform initiative before transitioning to AI-powered customer-facing B2B products. Define product direction, requirements, scope, milestones, quality metrics, and success measures. Partner with engineering, design, leadership, and sales to deliver products from concept through launch. Manage prioritization, sprint planning, stakeholder alignment, risk reporting, and product strategy while improving cross-functional processes and visibility.

Define and evolve enterprise architecture strategy, roadmaps, frameworks, standards, and reference architectures. Review solution designs, support architecture governance, assess project impacts, and ensure alignment with business needs and IT strategy. Collaborate with agile teams, business stakeholders, and technical experts while modernizing legacy environments. Monitor emerging technologies, provide recommendations on technical requirements, promote architecture best practices, and maintain architecture documentation.

Develop and industrialize statistical, predictive, and machine learning models, including GenAI, NLP, OCR, and visualization solutions, for insurance and reinsurance business needs. Collaborate with clients, underwriters, actuaries, and data experts; conduct research, improve model interpretability, communicate findings, and support AI innovation. Apply coding, data protection, security, documentation, and quality best practices while contributing to global data science initiatives.

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.

Design, develop, and maintain data pipelines and large-scale processing systems to ensure data accessibility, reliability, and efficient processing. Build ETL/ELT flows, integrate multiple data sources, support data warehousing and reporting, implement APIs and orchestration, optimize data architectures, and apply algorithmic/statistical methods (including AI) to enable analysis and downstream consumption.

Own the vision, strategy, roadmap, and delivery for Microblink’s Platform and Know Your Agent products. Lead and develop product managers, partner with engineering, design, sales, marketing, and customer success, and manage product metrics. Serve as the VP of Product’s second in command by running planning, prioritization, roadmap reviews, cross-functional alignment, product operations, escalations, and leadership communications.

Develop and evolve C++ APIs, tools, and real-time simulation middleware for cloth, hair, and rigid objects in AAA games. Research new simulation features, including machine learning applications; optimize performance; integrate SDKs with game development pipelines; troubleshoot bugs; document systems; and collaborate with global teams in an Agile environment.

Advise city governments across the Americas on designing, prototyping, and launching digital services. Guide system and data architecture, DevOps, infrastructure, AI and machine learning decisions, rapid prototyping, and end-to-end product delivery. Work hands-on with multidisciplinary teams while coaching clients and influencing technical decisions without direct authority. The role requires senior technical leadership, strong communication, and professional English plus Spanish and/or Portuguese.

Lead and develop machine learning, LLM, and automation projects using large-scale network and device telemetry. Clean and transform data, engineer features, build models for anomaly detection, forecasting, and optimization, and deliver reproducible analyses with Python and SQL. Collaborate with engineering and product teams to deploy, integrate, and monitor models in production while contributing to MLOps workflows.

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.

Own the vision, roadmap, commercialization, and executive strategy for Kaltura’s AI-powered learning and enablement platform. Lead 0-to-1 MVP delivery and post-MVP expansion across AI course generation, conversational tutoring, personalized learning, simulations, and enterprise foundations. Integrate acquired products and agentic AI capabilities while partnering with engineering and GTM teams on architecture, pricing, packaging, positioning, and enterprise sales. The role requires deep B2B SaaS product leadership, enterprise learning platform experience, production AI/ML delivery, and strong technical and commercial judgment.

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.

Own the strategy and execution of integrated B2B marketing campaigns for enterprise AI solutions and a self-service platform. Drive pipeline, product adoption, acquisition, and category awareness through ABM, content, events, paid media, lifecycle, social, partnerships, PR, and sales activation. Coordinate internal teams and agencies, optimize messaging and conversion paths, and measure performance across account engagement, pipeline, product adoption, and awareness.

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 and deliver internal systems, Go services, APIs, data models, dashboards, visualizations, and LLM-powered workflow features. The role includes writing specifications, estimating work, code reviews, testing, documentation, security, performance, accessibility, and collaboration with nontechnical stakeholders. The engineer will handle sensitive people data, mentor colleagues, and improve team practices in a globally distributed environment.

Own the anti-fraud product roadmap across signup, payments, account protection, compute-abuse prevention, risk scoring, manual review, customer verification, and appeals. Partner with Trust & Safety, Engineering, Data Science, Analytics, and Legal to define detection rules, models, incident responses, experiments, and compliance processes. Monitor fraud losses, chargebacks, false positives, review throughput, and detection speed while minimizing customer friction.

Designs and implements AI/ML compiler technologies for embedded hardware using C++ and LLVM, optimizing models for high-performance inference. Develops compiler features for ARM and RISC-V processors, including compiler passes, lowering, graph optimization, code generation, and deployment workflows. Collaborates with architecture teams, internal users, and global development teams to deliver proprietary compiler capabilities. Requires exposure to machine learning, edge AI, embedded systems, ML frameworks, and open-source contribution.

Lead and develop Customer Success Managers across Southern Europe, especially France and Spain. Ensure enterprise customers successfully launch and derive measurable value from the product, build executive and technical stakeholder relationships, translate product capabilities and performance data into business outcomes, establish scalable customer success processes, and handle complex escalations.

Develop quantitative models for sports pricing, odds compilation, and risk management. Analyze large datasets, identify trading insights, deploy models with engineers, monitor performance, conduct back-testing, and continuously improve model accuracy. Collaborate with data scientists and engineers to align models with business needs. The role requires Python, sports betting market knowledge, statistical modeling, and experience with odds and margin optimization.

Develop and deploy machine learning models for retrieval, reranking, indexing, search relevance, crawling, and content understanding. Build large-scale embedding and retrieval systems, evaluation pipelines, and quality metrics for agent-native search. Integrate models into high-throughput production services while balancing latency, quality, and cost. Collaborate on architecture and product decisions involving search, retrieval, and LLM-based systems.

Lead product marketing for the French market by developing localized positioning, messaging, sales enablement, launch campaigns, and customer insights. Partner cross-functionally with product, sales, marketing, engineering, design, communications, and partnerships teams. Track adoption and market performance, identify growth opportunities, and lead product marketing strategy for a major Paris customer event. The role requires French and English fluency, B2B technology marketing experience, and travel within France and internationally.

Own the end-to-end strategy, roadmap, and outcomes for a network intelligence and security product serving ISP customers. Partner with engineering, data science, security, and compliance teams on architecture, ML evaluation, privacy, and production delivery. Conduct customer discovery, analyze market and threat trends, define requirements and metrics, build business cases, and represent the product to technical customers, executives, sales, marketing, and public audiences.

Own and extend an existing machine-learning solution for first-order product recommendations. Build and productionize models, Azure Machine Learning training and inference pipelines, scheduled retraining, validation gates, monitoring, drift detection, and data-quality checks. Integrate outputs with data warehouses and Power BI, maintain reproducibility and versioning, support testing and rollout, and collaborate with AI/ML, BI, and business teams during a 15-week MVP.

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.