Top Machine Learning Jobs in Czechia

Develop, validate, and deploy advanced AI and machine learning solutions for aerospace engineering and business challenges. Build generative AI, agentic AI, and predictive analytics applications; conduct experiments and evaluations; analyze data; report findings; and present results. Ensure solutions meet responsible AI, cybersecurity, regulatory, and governance standards. Lead innovation through research, prototyping, multidisciplinary collaboration, patents, technical publications, and industry engagement.

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 governs enterprise data architecture, including conceptual, logical, and physical data models; master and reference data standards; metadata, lineage, data flows, and data quality controls. The role leads architecture designs across integrated technologies, documents current and future-state landscapes, resolves cross-functional data issues, assesses risks and trade-offs, and aligns programs with enterprise data principles and business objectives.

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.

Own the vision, strategy, and lifecycle of Coupa’s AI Document Intelligence products. Translate LLM, embedding, and agentic AI capabilities into reliable procurement and finance workflow experiences. Partner with data science, ML engineering, UX, engineering, go-to-market, and customer teams to prioritize use cases, define success metrics, launch AI features, and drive adoption. The role requires strong AI product expertise, customer empathy, communication, and the ability to influence cross-functional teams without formal authority.

Develop and deploy AI services across finance, HR, sales, service, legal, and quality assurance. Identify and prioritize use cases, build LLM and RAG solutions, integrate them into business processes, guide external partners, communicate technical decisions, and support organization-wide AI adoption.

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.

Designs, builds, and improves Thales’s observability platform. Responsibilities include enhancing monitoring, logging, tracing, alerting, dashboards, automation, enrichment, and machine-learning-based anomaly detection. The role collaborates with architects, service delivery, product, and development teams to establish an observability roadmap and strategy. Candidates need at least five years of experience, strong observability tooling expertise, Linux and scripting skills, cloud and on-premise administration knowledge, DevOps understanding, and excellent communication abilities.

Build and productionize agentic AI and NLP systems that process millions of documents and support data operations. Develop tool-calling agent loops, orchestration, memory management, RAG pipelines, metadata and entity extraction, and evaluation frameworks measuring accuracy, latency, and cost. Apply Python, SQL, and Snowflake to assess solutions, implement guardrails, manage AI risks, collaborate with technical stakeholders, and communicate delivery trade-offs.

Develop Python tools, libraries, and algorithms to optimize, deploy, profile, and benchmark neural network models on embedded and mobile devices. Contribute to open-source projects such as PyTorch, ONNX, and TensorFlow, enable AI models on edge platforms, explore research, and co-develop software for AI accelerators.

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.

Supports process engineering and production development for switchgear manufacturing. Responsibilities include optimizing production workflows using Lean Six Sigma, supporting automation projects, designing facility and workplace layouts, improving production supply methods, creating assembly fixtures and process instructions, preparing technical documentation, and independently leading smaller improvement projects. The role is onsite and suited to a university student with strong analytical, organizational, communication, and Excel skills.

Leads strategy, roadmap, and execution for digital technology transfer and Process Knowledge Management in biopharmaceutical manufacturing. Oversees digital manufacturing products, integrates MES, LIMS, process control, and data systems, ensures data integrity and GxP compliance, and partners with manufacturing, science, IT, and contract manufacturing organizations. Manages multidisciplinary teams, product backlogs, stakeholder alignment, and implementation of governed AI solutions in regulated environments.

Leads ABB Marine & Ports’ AI, data and information systems transformation. Builds the division-wide strategy, consolidates distributed capabilities into a strategic function, develops the team, prioritizes AI use cases, and drives measurable business value. Establishes shared data and AI platforms, governance, cybersecurity, and responsible AI practices while scaling adoption across business lines. Acts as the interface with ABB Group initiatives and ensures enterprise applications support the division’s AI and data agenda.

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.