Top Machine Learning Jobs in Czechia

Leads analytical methodology and evaluation for AI-based healthcare and pharmaceutical workflows. Owns model selection, cohort and HCP segmentation, agent decision logic, and evaluation frameworks. Partners with engineering, data architecture, and governance teams to productionize rigorous analytical methods, identify new data and techniques, communicate insights, and mentor data science and engineering colleagues.

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

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.

Supports analytics initiatives in energy markets by analyzing data, improving predictive models, applying machine learning techniques, and contributing to trading strategies involving physical energy assets. Works alongside mathematicians and experts on real-world problems while developing practical skills in statistics, probability, data analysis, and machine learning.

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.

Review and annotate images by identifying people, actions, and predefined categories. Follow detailed annotation guidelines, maintain accuracy and consistency, review completed work, and help create high-quality datasets for AI models. The role requires strong attention to detail, independent work, reliability, and EU citizenship with residence in the European Union.

Lead the Agentic Discovery product chapter, initially owning business tooling, dashboards, transparency, and controls for AI-powered commerce experiences. Define strategy, align ML, engineering, design, and go-to-market teams, scale experimentation, launch reliable LLM-powered experiences, and establish metrics tied to usage, conversion, revenue, and engagement. The role will expand to chapter-wide leadership across on-site, offsite, and integrated agentic discovery surfaces.

Designs, builds, and evaluates research prototypes and production-ready systems for portable AI agents, model routing, provider interoperability, memory, retrieval, tool use, and multi-agent workflows. Responsibilities include developing evaluation frameworks, conducting statistically rigorous experiments, creating schemas and APIs, optimizing model and agent systems, and translating research into secure, reliable, reusable components. The role collaborates across research, infrastructure, security, product, and engineering teams and communicates findings through technical reports, benchmarks, demonstrations, open-source releases, and publications.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Own the full enterprise customer journey from kickoff through adoption and ongoing value realization. Build executive and technical stakeholder relationships, lead QBRs and EBRs, translate product performance into business outcomes, manage risks and escalations, provide strategic ecommerce recommendations, maintain success plans, and partner with Account Management on retention and expansion.

Own enterprise customer relationships from kickoff through adoption and ongoing value realization. Lead success plans, QBRs, executive conversations, escalations, stakeholder alignment, training, and strategic recommendations. Translate technical capabilities and performance data into business outcomes such as revenue, conversion, and customer experience. Partner with Account Management to support retention and expansion while improving scalable onboarding and support processes.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

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.

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.

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

Leads a team of data engineers and advanced analytics professionals while designing scalable data platforms, pipelines, data models, machine learning solutions, and AI-driven products. Responsibilities include Python, SQL, Snowflake, and AWS development; predictive modeling; graph analytics; entity resolution; semantic search; dashboards; production deployment; and technical strategy. The role requires hands-on architecture, data management, statistical analysis, stakeholder collaboration, and people leadership.

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.

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.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

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 maintain AI systems using software engineering best practices. Build, deploy, version, test, document, and monitor AI models and applications. Collaborate in Agile teams, prepare technical reports and presentations, and stay current with AI and machine learning advances. The role involves backend development, scalable cloud-based solutions, data pipelines, RESTful APIs, and potential work with RAG, LLM fine-tuning, and prompt engineering.

Translate legal workflows into AI and ML requirements, evaluate model outputs, create legal AI quality standards, develop rubrics and annotation guidelines, identify hallucinations and edge cases, and provide structured feedback to engineering and product teams. Collaborate with ML engineers, product managers, data scientists, and annotation teams to improve prompts, retrieval strategies, agent workflows, and legal AI reliability.