Top Machine Learning Jobs in Prague

Design, build, and maintain production ML pipelines and data pipelines, train and optimize large-scale personalization and recommendation models, deploy and operate low-latency model serving, monitor production model performance, run A/B experiments, and collaborate across data science, engineering, and product teams to continuously improve personalization systems.

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.

Support the RAI team with data and training tasks: build datasets, run experiments, develop tooling and evaluation, and quickly learn the engineering stack.

Develop, validate, deploy, and maintain machine learning features that automate orthodontic treatment planning. Responsibilities include decomposing business needs into ML problems, building deep learning models for 3D meshes, text, and tabular data, analyzing model performance, collaborating on production architecture and integration, researching scientific advances, and transferring technology across product teams.

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.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

Own a from-scratch rebuild of Nory's mobile app using React Native and TypeScript, ensuring reliability on low-spec devices and poor connectivity. Set architecture, design system, and migration plan, ship an MVP, integrate AI agents to speed development, and grow the codebase into a platform for cross-functional teams while maintaining high UX and reliability standards.

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.

Design, train, and evaluate large multimodal and vision-language-action models for robotic agents. Develop reinforcement and imitation learning methods, embodied datasets, simulation environments, world models, planning systems, and sim-to-real experiments. Build robust research software and distributed training infrastructure, validate approaches on physical robots, and collaborate with research and engineering teams to deploy reliable systems. Communicate findings through technical reports, open-source releases, demonstrations, and publications.

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.

Develop and own production-ready machine learning, LLM, NLP, and RAG solutions within Palantir Foundry and AIP. Responsibilities include data exploration, model development, evaluation, deployment, monitoring, workflow integration, and optimization for accuracy, scalability, reliability, latency, and cost. Collaborate with engineers and business stakeholders, make architecture decisions, translate business needs into AI solutions, and communicate model behavior and limitations.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

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.

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.

Lead Data, Analytics, AI, and GenAI presales activities for strategic enterprise opportunities. Facilitate discovery workshops, translate business needs into scalable architectures, lead RFP and RFI responses, and present solutions to executives. Design end-to-end AI and cloud architectures across Azure, GCP, Databricks, and open-source ecosystems. Advise on AI transformation use cases, roadmaps, governance, and implementation, while mentoring delivery teams and developing reusable frameworks, offerings, thought leadership, and customer success materials.

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

Build Solidgate’s new ML/AI function, developing production machine learning models, recommendation systems, and real-time transaction-routing decision systems. Own experimentation from ideation through A/B testing and deployment, engineer features from high-cardinality transactional data, integrate emerging AI technologies, and collaborate with Product, Engineering, and Data Platform teams on training and analytics pipelines.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

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.

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.

Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.

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.

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.