Top Machine Learning Jobs in Czechia

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 product discovery, strategy, prioritization, delivery, launches, and measurable outcomes for an AI-powered regulatory compliance platform. Partner closely with founders, engineering, design, customers, and legal engineering to identify high-value problems, develop solutions, ship improvements, and shape the long-term roadmap. The role requires strong product judgment, customer understanding, data-informed decisions, and comfort operating autonomously in an ambiguous startup environment.

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.

Build and ship production generative AI and agentic systems for enterprise customers. Responsibilities include developing RAG applications, evaluation harnesses, backend services, data pipelines, and cloud-native deployments; implementing LLMOps and AgentOps monitoring, tracing, guardrails, drift detection, and cost controls; making architecture decisions; documenting and handing over solutions; and mentoring AI engineers.

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.

Develop, validate, deploy, and maintain machine learning solutions for medical device manufacturing and CAD/CAM systems. Build deep learning and multimodal AI models using computer vision, 3D geometry, meshes, images, and structured data. Integrate models into production systems, conduct performance analysis, maintain ML workflows, and translate research into practical manufacturing applications while collaborating with engineering, manufacturing, and product teams.

Lead business analysis and project management for Intelligent Automation and AI transformation initiatives. Responsibilities include requirements gathering, BPMN process modeling, project governance, executive workshops, stakeholder management, business cases, QA traceability, organizational change, and mentoring. The role advises C-level clients, coordinates automation developers and SMEs, supports AI risk and ethics considerations, and contributes to consulting practice growth, pre-sales, and business development.

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 platform-level optimization for production LLM and VLM inference. Responsibilities include improving latency, throughput, memory efficiency, GPU utilization, reliability, quality, and cost; deploying and extending inference engines; building compression and acceleration workflows; developing benchmark and rollout frameworks; diagnosing performance bottlenecks; and providing technical direction across machine learning, GPU, infrastructure, research, product, and customer teams.

Designs and delivers production credit analytics and machine learning platforms for regulated financial-services clients. Responsibilities include architecture, Python and SQL development, reproducible ML workflows, MLOps, data quality, model governance, Snowflake application reliability, monitoring, access control, and technical leadership. The role works directly with clients and modelling specialists to productionize credit-risk models, support multiple portfolios, and evaluate responsible uses of LLM-assisted analytics.

Own the strategy, roadmap, metrics, and adoption of JetBrains Context, a managed retrieval layer for AI coding agents. Partner with engineering, ML, platform, IDE, marketing, sales, and customer success teams to improve indexing, retrieval quality, integrations, reliability, cost, and latency. Define product direction, evaluate competitors, establish quality metrics, communicate with customers and developer communities, and drive enterprise adoption.

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.

Develop Python-based data processing, analysis, modeling, forecasting, and visualization tools. Build and optimize Snowflake data pipelines for ingesting, cleaning, transforming, and loading large datasets. Prototype and deploy end-to-end data operations, develop Streamlit applications, and collaborate with technical and analyst teams to gather requirements. The role supports market and vendor data products and may involve data quality checks, outlier detection, machine learning, and generative AI.

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.

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 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 research across the full machine learning lifecycle, including ideation, model design, implementation, evaluation, training, inference optimization, and deployment. Develop novel machine learning methods and foundation models, establish evaluation protocols, scale training with GPUs or TPUs, and improve research engineering infrastructure for rapid experimentation.

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

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.