Top Machine Learning Jobs in Poland

You will optimize ML infrastructure, automate ML pipelines, manage versioning, and collaborate with engineers to ensure model scalability and reliability.

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 large-scale ML projects overseeing their entire lifecycle. Influence technical decisions, design ML pipelines, and mentor other engineers.

The role involves building scalable ML models, mentoring engineers, translating business requirements into specs, and ensuring solutions meet performance constraints.

Build and deploy production-grade generative AI and agentic systems for enterprise clients. Responsibilities include developing RAG applications, evaluation harnesses, backend services, data pipelines, and cloud-native AWS deployments; implementing LLMOps and AgentOps practices; monitoring quality, drift, cost, and latency; integrating APIs; improving reliability; contributing to architecture and reusable blueprints; and mentoring AI engineers in a forward-deployed consulting environment.

Build and deploy production-grade generative AI and agentic systems for enterprise customers. Responsibilities include developing RAG applications, backend services, data pipelines, evaluation harnesses, monitoring, guardrails, and cloud-native AWS infrastructure. The role owns model and agent reliability, cost, latency, drift detection, and release quality. It also involves architectural decisions, customer handover, documentation, mentoring engineers, and collaboration with forward-deployed executives and engineers.

Integrate machine learning models into production SaaS features by deploying model-serving endpoints, building Python or Node.js microservices, designing REST APIs, and supporting React interfaces. Optimize inference performance, scalability, and reliability through caching, asynchronous processing, and batching. Containerize and orchestrate services, maintain API contracts, implement CI/CD and automated tests, and collaborate with data science, product, QA, and engineering teams.

Design, implement, and optimize ETL/data pipelines, data quality metrics, APIs, and enrichment for AI/ML platforms; research big-data technologies; run trials, write technical documentation, and collaborate in project 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.

Lead end-to-end development and ownership of in-cabin camera ML features (driver monitoring, child presence, occupant classification). Architect ML systems, drive research and IP, optimize models for edge deployment, collaborate cross-functionally, meet safety and performance KPIs, and manage technical partnerships and product roadmap.

Build and maintain scalable data pipelines and warehousing to support ML and LLMs. Assist in model development, deployment, data transformation, quality, and performance optimisation, collaborate with data scientists, document workflows, and evaluate new AI technologies.

Lead design, development, deployment, and maintenance of production-grade ML solutions across the full ML lifecycle for shipping and logistics. Build data pipelines, MLOps practices, model serving, monitoring, automated retraining, and mentor teammates while collaborating with business stakeholders.

The Senior Machine Learning Engineer will build and deploy sophisticated AI systems, focusing on Large Language Models, to drive business value and enhance customer experience through machine learning solutions.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

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.

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 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.

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, 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 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 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, train, fine-tune, distill, and deploy LLM-based and agentic ML models. Build and operate agent workflows in Python, run evaluation and benchmarking, select models and cost strategies, and manage production monitoring and observability for model performance and reliability.