Top Machine Learning Jobs in Czechia

Lead design and implementation of scalable knowledge graph solutions for pharmaceutical R&D. Build graph data models, integrate heterogeneous scientific datasets, drive adoption of RDF/property-graph technologies, partner with platform and AI/ML teams, ensure performance and governance, define roadmap, and mentor engineers in ontology and graph engineering best practices.

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.

Develops and scales predictive analytics and machine learning models for commercial decision-making. Responsibilities include forecasting, pricing optimization, profitability and tender analytics, customer behavior modeling, opportunity identification, model lifecycle management, and embedding analytical solutions into business processes. Partners with Commercial, Marketing, Finance, and IT stakeholders to translate business needs into scalable, interpretable insights and measurable growth outcomes.

Trade energy commodities across global markets, monitor market trends and weather patterns, analyze data, and make rapid decisions under uncertainty. Improve machine learning price-prediction models and contribute to developing the company’s proprietary trading platform. The role suits graduates with university backgrounds in mathematics, physics, or technical fields, strong analytical and probability skills, an interest in trading, and a results-driven mindset.

Leads large-scale technical programs from requirements and solution definition through integration, release, and production deployment. Drives AI, automation, and data analytics initiatives; manages plans, budgets, risks, dependencies, vendors, and global stakeholders. Partners across Sales, IT, Product, Engineering, and Support, translating customer needs into requirements and presenting program status and data-driven insights to senior leadership.

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.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Lead simulation-driven development of power modules using multiphysics (electrical, thermal, mechanical) models, digital twins, AI-augmented optimization, and automation to accelerate SiC/GaN power-electronics design and reliability prediction while collaborating with global teams.

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 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.

Support multilingual data quality by analyzing and cleaning text data, developing parsers, evaluating text classifiers, translating taxonomies (Skills, Titles, Occupations), and partnering with Linguistics and Engineering to deliver high-quality global data.

The role involves data collection, evaluation, comparison, and annotation to enhance AI-generated content in Vietnamese, ideal for flexible workers.

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Develop core methods for temporal ontology extraction and contradiction detection. Collaborate with research teams and publish findings. Prototype ideas, create datasets, and validate methods.

Design, develop, test, deploy, and maintain ML/AI software solutions. Ensure technical and product excellence, performance, and engineering best practices. Collaborate across global teams and contribute to strategic goals at an early-stage startup.

Lead design, development, and deployment of AI solutions; evaluate feasibility; build data pipelines; optimize model performance; mentor junior engineers.

Lead global marketing strategy and GTM to scale Aisle into a market leader. Own brand, PR, product positioning, full-funnel demand generation, ABM, marketing tech stack, RevOps, attribution, budget/agency management, and grow a multidisciplinary marketing team aligned to revenue targets.

Design, build, test, and maintain ML/AI software solutions; ensure product and technical excellence; apply ML, NLP, and large-scale system practices; collaborate across global teams; follow engineering best practices and support strategic goals.

Operate Dataiku and Databricks environments supporting production analytics and machine-learning pipelines. Responsibilities include promoting data-science projects to production, packaging Python environments, configuring monitoring and alerting, triaging pipeline failures, administering platform users and policies, coordinating with data owners, tracking operational demand, writing runbooks, and supporting automation design reviews. The role requires strong operational judgment, Python and AWS experience, clear incident communication, and effective support for data scientists transitioning projects from development to production.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Develop and ship on-board, real-time robotics policies by training Vision-Language-Action models on human demonstration data. Map HD-sEMG signals to force, close the sim-to-real loop in NVIDIA Isaac Sim, build large-scale multi-node GPU training pipelines, design robustness and generalization evaluations, and optimize models for deployment on GENESIS robots.

Lead a multi-agent GenAI PoC for an FMCG client: own client relationship, design solution architecture (Discovery/Ideation/Validation/Orchestration), define retrieval and evaluation strategies, run benchmarking with SMEs, manage PoC/pilot delivery, and shape follow-on phases including conversational layers and scaled evaluation.