Top Machine Learning Jobs in Poland

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

Partner with Market Units to analyze product, customer, and market data, produce pricing dashboards and reports, run workshops, track and improve pricing initiatives, and develop PriceFX analytics and AI-driven pricing agents while training users and collaborating with IT and data science teams.

The Fund Accountant will handle trade capture, reconciliations, pricing, and assist with NAV calculations under supervision, ensuring accuracy and timely reporting.

The Fund Accounting Associate will prepare reconciliations, process accruals, verify corporate actions, assist with audits, and maintain client relationships.

The Fund Accounting Supervisor oversees cash and position reconciliations, reviews fund pricing and compliance, mentors team members, and ensures deadlines are met.

Develop, optimize, benchmark, and scale large AI models for Graphcore hardware; run distributed training across thousands of accelerators, design experiments, collaborate with research and software teams, and contribute performance improvements and code reviews.

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

Develop, maintain and deploy expert systems for kiln and grinding optimization. Implement AI/ML and advanced control strategies, integrate soft sensors and process models, ensure DCS/PLC communication, provide remote support, tune MPC/Fuzzy/PID controllers, train operators, and maintain technical documentation.

Develop, implement, and document expected loss forecasting models (PD/LGD/EAD) for commercial portfolios to meet CECL/CCAR and regulatory requirements. Analyze model performance, refine segmentation, support stress-testing submissions, and respond to regulators. Use Python/SAS/R/SQL and cloud tools, leverage automation and AI to improve efficiency, and provide model governance and validation-ready documentation.

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.

As a Portfolio Accounting Associate, you will process cash and position reconciliations, resolve fund issues, and work on projects under supervision, aiming for efficient workflows.

Design, implement, maintain, and support AI/ML solutions, internal tools, and scalable ML infrastructure. Collaborate with researchers and business teams to productionize innovative ideas, provide AI/ML subject-matter expertise, lead projects when needed, and promote company-wide best practices. Manage orchestration, CI/CD, infrastructure, deployment, versioning, and monitoring across cloud and on-premises platforms.

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.

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.

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.

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.

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

Lead a Data Science pod to design, build, and deploy ML, forecasting, and optimisation solutions for supply chain planning. Partner with stakeholders to embed models into systems, ensure adoption and measurable outcomes, coach data scientists, and drive MLOps and best practices to scale autonomous planning capabilities.

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.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

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.

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.

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