Top Machine Learning Jobs in Poland

Design, prototype, and deploy ML models for personalization in mobile games. Implement and evaluate algorithms (including RL and generative methods), run A/B tests, convert prototypes to production via MLOps, monitor player data, and collaborate with product, engineering, and analytics teams to improve retention and monetization.

Design, build, and productionize LLM-based AI systems, reusable services, retrieval pipelines, agents, and evaluation-driven workflows. Partner with product teams to integrate shared AI capabilities, apply guardrails for reliability and governance, and drive AI-assisted engineering practices from prototype to production.

Design, develop, and deliver enterprise-grade Generative AI and ML platform services, reusable frameworks, APIs, and agentic applications. Build scalable inference, deployment, monitoring, and CI/CD capabilities, implement event-driven streaming solutions, collaborate with architects and data scientists, and ensure security, governance, scalability, and operational excellence for AI/ML lifecycles.

Build, maintain, and optimize ETL pipelines and analytical data warehouse infrastructure. Deploy, monitor, and operate machine learning models in production, automate model lifecycle and data processing, collaborate with Data Science/BI/Engineering/Risk/IT, troubleshoot performance issues, and maintain technical documentation.

Design, optimize, validate, and update streaming ASR models. Integrate ASR into products, build audio data pipelines, collaborate with product and engineering teams, and apply state-of-the-art techniques to improve recognition across domains and dialects.

Owner of production ML subsystems for a proactive assistant: build end-to-end ML pipelines (data, training, evaluation, inference), ship reliable GPU-based systems, debug production model failures, iterate from research to production, meet accuracy/latency/reliability targets, and mentor other ML engineers.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

Design and implement end-to-end MLOps frameworks on Azure Databricks, architect secure scalable ML platforms, collaborate with cross-functional teams to move models from experimentation to production, and maintain up-to-date best practices in Databricks and Azure tooling.

The Senior AI/ML Engineer will design and deploy AI systems for business impact, lead MLOps practices, and collaborate with teams to drive AI solutions.

The ML Ops/Data Engineer will ensure reliability and scalability of ML systems, manage data pipelines, monitor performance, and collaborate with teams for optimization.

The Senior ML Engineer will create and improve ML models, collaborate with teams, monitor models in production, and maintain documentation.

Design and scale predictive ML systems for VIS.X, taking end-to-end ownership of various high-impact ML initiatives and shaping the ML architecture.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

Lead research, prototype, and deploy state-of-the-art NLP/LLM models for intent detection, chatbots, routing, and automation. Design experiments (offline/online/A-B), analyze large multilingual datasets, productionize models with engineering partners, monitor model health, and mentor junior scientists while translating research into scalable customer-experience solutions.

Build and operate ML infrastructure powering model training, evaluation, deployment, and inference. Design scalable, reliable, and cost-efficient serving, pipelines, benchmarking, observability, and tooling to enable rapid experimentation and productionization of AI models.

Design, build, and operate an ML platform on GCP (Vertex AI). Develop and automate ML pipelines, provision infra with Terraform, create CI/CD workflows with GitHub Actions, build production Python APIs, implement model monitoring and observability, and support GPU-based training in HPC/Slurm environments.

Design, build, deploy, and maintain scalable AI/ML systems and pipelines. Implement MLOps/LLMOps/AgentOps practices, run large-scale experiments, enable model serving and monitoring, and collaborate cross-functionally to integrate ML into business applications.

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

Design, build, and operate scalable MLOps systems and workflows for large-scale distributed ML. Implement observability (Prometheus, Grafana, OpenTelemetry), CI/CD, container orchestration, and cloud infrastructure (AWS/GCP, Terraform). Collaborate with data scientists on LLM/generative AI deployment, review architectures, and mentor junior engineers.

Design and implement a Databricks-based MLOps platform to host, orchestrate, and manage up to 1,500 ML scoring processes. Build IaC (Terraform), CI/CD-driven deployments (MLflow or equivalent), scheduling, parallel execution, resiliency, monitoring/alerting, and model performance hooks. Collaborate with data scientists, DevOps, and architects to enforce security, compliance, cost-efficiency, and SLAs.

Lead design and productionization of ML pipelines, evaluation systems, and AI agents for cybersecurity. Own experimentation-to-production lifecycles, integrate ML across product teams, scale ML infrastructure, optimize agent performance, and mentor junior engineers while collaborating with CTO and product leadership.

Design, build, and maintain end-to-end AI/ML solutions including data pipelines, model development, edge deployments, and integrations. Define camera and imaging setups for computer vision use cases, coordinate installations, monitor model performance, and collaborate with IT, software, and infrastructure teams to ensure scalable, secure, and maintainable solutions.

Lead design and delivery of ML solutions for perception, prediction, and planning for assisted and automated parking. Define CV and multimodal sensor-fusion approaches, drive architecture from data pipelines through deployment, establish evaluation metrics, mentor engineers, and collaborate with global teams to improve algorithms and production performance.

Design and optimize ML models for perception, prediction, and planning in automotive parking systems. Build computer-vision and multi-sensor processing solutions, trajectory forecasting, training/evaluation pipelines, analyze large datasets, validate models in real-world scenarios, and collaborate with software, systems, and product teams to improve algorithms and tools.