Top Machine Learning Jobs in Poland

Design, train, and improve computer-vision and generative ML models; handle end-to-end ML pipeline from data collection to deployment; prototype research ideas and convert successful prototypes into product-ready solutions; optimize models for efficient on-device inference and collaborate with product teams to integrate models.

Design, build, and maintain scalable ML infrastructure and pipelines; serve models at scale; implement CI/CD, GitOps, and containerized deployments; build monitoring, observability, and drift-detection tools; collaborate with data scientists, data engineers, DevOps and stakeholders to productionize AI solutions and improve model lifecycle.

Design, build, and maintain scalable ML infrastructure and production ML pipelines. Serve and monitor models at scale, implement CI/CD/GitOps for ML, and collaborate with data scientists, data engineers, and DevOps to deploy AI-driven solutions. Improve observability, drift detection, and operational tooling for continuous model improvement.

Lead delivery of a multi-agent GenAI PoC for an FMCG/consumer health client: own client relationship, design solution architecture (Discovery, Ideation, Validation, Orchestration), drive evaluation and pilot, manage PoC milestones, present executive-ready materials, and identify Phase 2 opportunities.

Design, build, and maintain production ML systems including LLMs, RAG architectures, and agentic systems. Collaborate with engineers and product teams, run experiments, set up reproducible pipelines, monitor production models, and document processes while proposing improvements.

As a Senior Python Engineer, you'll design and implement scalable solutions, collaborate across teams, and manage infrastructure on cloud platforms, ensuring high-quality deliverables.

Develop and optimize the ML training framework in high-performance C++, implement new ops and layers, debug and tune model performance on Tenstorrent chips, collaborate with compiler and kernel teams, and support integration and productionization of real-world models.

Lead production ML systems end-to-end: design, train, deploy, monitor, and improve models (recommendation, ranking, personalization). Drive MLOps best practices, mentor teammates, make architectural decisions, reduce operational complexity, and collaborate across data science and engineering to deliver reliable, scalable ML features.

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.

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.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

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.

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.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Design and develop software solutions for AI/ML applications, propose new approaches to optimization problems, and collaborate with global teams in research and development.

Lead design and delivery of multi-turn, state-aware conversational AI and agentic systems. Build, benchmark, and maintain LLM-driven agents using Python, MLflow, Azure and Databricks. Mentor team members, drive adoption of best practices, collaborate cross-functionally, and engage customers to align solutions with enterprise sustainability use cases.

Build and operate ML platform systems for training, evaluation, deployment, and serving. Improve reliability, scalability, cost-efficiency, observability, and automation for GPU and cloud workloads. Develop internal tools and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and developer experience.

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.

Maintain and scale production ML systems: monitor model performance and explainability, automate MLOps pipelines, troubleshoot production workflows, perform EDA and data quality checks, build and maintain static and temporal features, support feature engineering and artifact monitoring, and develop internal tools and registries to ensure reliable, explainable ML deployments.

Support and maintain production ML systems: monitor performance and explainability, automate MLOps pipelines, troubleshoot production issues, build and maintain feature and monitoring repositories, perform EDA, implement production adjusters, and support internal tooling and feature engineering for time-series forecasting.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.