Top Machine Learning Jobs in Warsaw

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

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.

Design and develop agentic workflows for intelligent recruiting systems, focusing on ML, NLP, collaboration, and integration with product teams.

Lead full-cycle recruitment for AI/ML roles across Europe and LATAM: build pipelines, partner with hiring managers, screen candidates, manage interviews and offers, and improve AI-focused hiring processes.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Design and deploy production-grade systems integrating machine learning models into scalable software pipelines, while focusing on maintainability, performance, and collaboration with 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.

Lead strategy and delivery of enterprise AI and intelligent automation programs. Identify high-value use cases, design and deploy RPA and AI/ML solutions, build automation roadmaps and governance, drive cost savings and ROI, integrate automation into transformation programs, and mentor teams while advising senior stakeholders and clients.

Design, build, and maintain MLOps tools, automation, and end-to-end ML pipelines. Support large-scale GPU clusters, implement monitoring/observability, streamline reproducible training and deployment, and collaborate with product and engineering teams to optimize ML workflows for scalability and cost-efficiency.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

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.

Lead the technical vision, architecture, and delivery of large-scale recommendation and personalization systems. Build scalable retrieval, ranking, and real-time pipelines, own ML infrastructure and MLOps, design evaluation and A/B testing, mentor ML engineers, and collaborate with product and engineering to align models with business metrics.

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

Conduct applied AI/ML research to solve home design challenges, work with raw data, test hypotheses, and build prototypes/MVP solutions.

The Machine Learning Tech Lead will enhance robotic capabilities, lead a technical team, and develop machine learning systems for real-world applications.

The Machine Learning Manager will lead R&D to enhance robots' manipulation capabilities, deploy ML systems, and develop interfaces for integration with robots and management systems.

Maintain and scale production ML systems: monitor model performance and explainability, automate MLOps pipelines, troubleshoot production workflows, build feature and inference monitoring, perform EDA on historical time-series data, and support internal tools and deployment-specific adjusters.

The Senior ML Data Engineer will enhance data workflows, collaborate with teams to build datasets, influence data strategies, and implement pipelines for effective data usage in AI/ML products.

Lead Salesforce product initiatives for Channel and Enterprise sales: gather and document requirements, build roadmaps, define solutions, drive implementation, enable adoption, produce reports, and partner with sales, sales ops, IT and enablement to improve selling effectiveness and reduce CRM admin burden.

You will design and optimize LLM-based machine learning solutions, focusing on natural language processing, deep learning methods, and inferencing pipelines.

Lead global marketing strategy and GTM to scale Aisle into a market leader. Own brand, PR, product positioning, full-funnel demand generation, ABM, marketing tech stack, RevOps, attribution, budget/agency management, and grow a multidisciplinary marketing team aligned to revenue targets.

Develop and productionize deep learning methods for robotic perception and manipulation. Build, evaluate, and monitor ML models in production, automate retraining pipelines, detect anomalies and influence data-driven company strategy while collaborating with hardware and engineering teams.

Design, build, and deploy production-grade ML and Generative AI solutions. Develop end-to-end pipelines, integrate LLMs, implement MLOps/CI-CD, monitor models, support releases, and collaborate with cross-functional teams on cloud-native AI platforms.