Top Machine Learning Jobs in Krakow

Build and enhance RAG systems, AI components, backend services, data pipelines, REST APIs, evaluation harnesses, and production AWS deployments. Write tests, investigate model failure modes, support containerized CI/CD releases, document solutions, and contribute to architecture and client handoffs. The role offers senior guidance while progressively increasing ownership of technical decisions and system components.

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.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Develop and improve Sophea AI’s large language models, including pre-training, fine-tuning, evaluation, inference optimization, and production deployment. Build ML pipelines for serving, monitoring, and lifecycle management while optimizing latency, throughput, cost, quantization, and GPU utilization. Work with multilingual datasets and benchmarks, with a particular focus on Polish-language models. The role requires strong hands-on LLM engineering experience and production ML systems expertise.

Design, back-test and implement pricing, risk management, and agency trading strategies; collaborate with developers to integrate models into the trading platform; participate in product ideation, modeling, parameter tuning, and risk evaluation.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.

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.

Lead and contribute to research applying machine learning to numerical simulations for power-grid engineering. Develop ML-driven surrogate models and pipelines, optimize simulation efficiency, collaborate with global teams and external partners, publish results, and support product development and automation of simulation processes.

Lead R&D projects to create mathematical models and numerical simulations for power grid reliability and stability with high renewable penetration. Develop, implement and validate simulation algorithms, collaborate globally, integrate optimization/ML methods, prototype solutions, publish results, and work with external partners.

Lead full-cycle recruitment for AI/ML roles across Europe and LATAM: build pipelines, partner with hiring managers, screen candidates, manage interviews and offers, and improve AI-focused hiring processes.

Design and deploy production-grade systems integrating machine learning models into scalable software pipelines, while focusing on maintainability, performance, and collaboration with teams.

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.

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

Design, build, deploy, and optimize production-grade machine learning systems for a proactive AI platform. Own projects end to end, including data curation, training, evaluation, inference, monitoring, troubleshooting, and iteration. Translate research into reliable systems, optimize latency and cost at scale, collaborate across research, engineering, and product, and mentor other engineers.

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.

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.

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

Own and grow the RateHawk Selections product: define strategy and roadmap, run fast experiments, prioritize opportunities from data and research, align Product/Engineering/Commercial/Operations, and co-own delivery to drive measurable revenue.

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.

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

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.

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.