Top Machine Learning Jobs in Prague

Lead end-to-end product lifecycle for AI/ML-powered pricing and price optimization products in manufacturing and distribution. Define roadmap, translate AI model outputs into customer-facing features, set objectives and metrics, and align sales, engineering, UX, and analytics to drive measurable pricing outcomes.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Design, build, and productionize end-to-end ML solutions on AWS: data exploration, model development, experiment design, packaging, deployment, monitoring, and cross-functional collaboration to drive product outcomes.

The Machine Learning Data Annotator produces high-quality annotations for AI-driven products, ensuring data accuracy and collaborating with teams for continuous improvement.

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

Lead horizon scanning and external intelligence to identify AI and disruptive tech opportunities for R&D. Translate trends into actionable briefs, design and run pilots with stage-gates, and scale successful capabilities into production. Influence senior leadership with strategic foresight, build external partnerships, and embed a future-back innovation culture across cross-functional teams to measurably improve R&D outcomes.

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.

Lead the Weaviate Database product: own roadmap and strategy, translate customer and research insights into features across schema, storage, replication, and search quality, coordinate cross-functional teams, drive developer adoption, and measure feature outcomes post-release.

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

Lead the Ratings & Reviews product line: define vision and roadmap, run product discovery with brands and retailers, write requirements, work with design and engineering, measure outcomes, and drive cross-functional go-to-market and iteration.

Develop, design, test and implement imaging and 3D reconstruction algorithms using C++ and Python. Analyze real data, optimize for high-performance computing, integrate algorithms into applications, and collaborate across algorithms, physics, and electronics teams while participating in design and code reviews.

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.

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.

Lead product strategy and roadmap for query interpretation and search quality. Own models, tools, and metrics to optimize recall and precision. Collaborate with ML, infra, and product teams, triage quality issues, benchmark competitors, and build evaluation frameworks and explainability for search results.

Lead the Recommendations engineering team to build scalable, reliable recommendation-as-a-service products. Own recommendation quality and business impact, set technical direction, translate product goals into engineering plans, ensure SLAs, hire and coach engineers, and drive system reliability and scalability.

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.

Oversee a team of AI-focused automation analysts, ensuring high availability and performance of production services, leading incident management, and driving operational excellence initiatives.

Lead go-to-market planning for JetBrains AI Ecosystem launches, create clear messaging and launch assets, coordinate stakeholders, scale campaigns, gather market and customer insights, and track campaign performance to improve processes.

Lead data science initiatives to develop AI-driven solutions for healthcare. Collaborate across teams to transform data into actionable insights and manage resources effectively.

Design and develop AI-driven features to enhance customer experience, execute ML projects, and optimize AI performance while collaborating cross-functionally.

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.

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.

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.

The Head of Computer Vision will lead a team of ML Engineers, drive the long-term ML strategy, focus on breakthrough innovations, and establish cross-functional partnerships while mentoring and scaling the team effectively.

Design, build, and operate scalable, secure ML/LLM production pipelines using Databricks, MLflow, Azure DevOps and vector stores. Implement CI/CD, monitoring, observability, explainability (SHAP), and responsible AI practices while optimizing platform components and collaborating with AI and DevOps teams.