Top Machine Learning Jobs in Poland

Enhance and operate a globally deployed recommender system by improving MLOps, CI/CD, experiment tracking, model deployment, scalable ML pipelines, monitoring, and observability. Provide technical guidance and mentorship while collaborating with data scientists, engineers, product managers, and business stakeholders to deliver reliable, reproducible, production-ready machine learning systems.

Investigate KYC/AML and sanctions screening matches during onboarding and maintenance; gather evidence from internal systems and external databases; assess customer risk and make disposition decisions; collaborate with business and compliance partners; work within agreed timeframes and apply training to build sanctions and KYC expertise.

Design, develop, and productionize AI/ML perception algorithms and multi-sensor fusion for camera and radar (and LiDAR) to enable robust autonomous robotics. Define validation, benchmarking, and safety-focused practices; integrate frameworks and SDKs; support PoCs, demos, and early deployments while collaborating with cross-functional teams.

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.

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.

Design and deliver enterprise data management and analytics solutions for financial analytics. Build data pipelines, sourcing/scraping/OCR workflows, ML models and POCs, collaborate with engineering and product teams, and deploy solutions on cloud (AWS) using MLOps practices. Translate domain needs into platform strategy and drive implementation across teams.

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.

As an Applied AI Engineer, you will architect, build, and deploy enterprise-grade AI solutions, work with customers to address complex challenges, and collaborate with cross-functional teams to innovate using AI tools.

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.

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

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.