Top Machine Learning Jobs in Czechia

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.

Own production optimization of LLM and VLM inference systems, improving latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost. Deploy and benchmark inference engines, build compression and quantization workflows, implement acceleration techniques, diagnose serving bottlenecks, and collaborate with kernel, platform, research, product, and customer teams.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Designs enterprise-grade cloud, data, and AI solutions for airline operations. Responsibilities include assessing technology landscapes, identifying modernization and integration opportunities, translating business priorities into technical architectures, creating solution blueprints and documentation, promoting enterprise architecture best practices, and guiding customer teams through transformation initiatives.

Design and lead multi-domain cloud, data, and AI architectures for aviation operations. Translate business needs into scalable technical solutions, guide technology decisions, oversee platform integration, and provide technical leadership and mentorship to customer development teams. Collaborate with technical and business stakeholders while balancing performance, security, scalability, and cost.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

The Power Market Analyst will analyze energy market trends, conduct quantitative analysis, and provide strategic insights for trading decisions across various commodities.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

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.

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.

Build and maintain scalable backend systems that integrate real-time data, machine learning, and optimization models to optimize renewable and battery storage operations. Own architecture through deployment and maintenance, collaborate with engineers and data scientists, troubleshoot performance issues, and improve monitoring, reliability, and system performance.

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

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

Design, build, deploy, and optimize production-grade machine learning systems for a proactive AI platform. Own projects end to end, including data curation, training, evaluation, inference, monitoring, troubleshooting, and iteration. Translate research into reliable systems, balance latency, cost, safety, and scale, and collaborate with research, engineering, and product teams. Mentor engineers and uphold strong standards for code quality, architecture, and technical judgment.

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.

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.

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.

The role involves shaping the technical direction of a core domain, leading architectural design, ensuring system security and scalability, and embedding AI in developer workflows while supporting various engineering teams.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.