Top Machine Learning Jobs in Krakow

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.

Work with traders, asset operators, and developers to analyze electricity market outcomes, improve trading and operational strategies, and drive customer success. Translate market workflows into product requirements, collaborate with engineering to enhance analytics, dashboards, and optimization models, and lead customer onboarding, implementation, and ongoing technical account management.

Support and maintain production ML systems: monitor performance and explainability, automate MLOps pipelines, troubleshoot production issues, build and maintain feature and monitoring repositories, perform EDA, implement production adjusters, and support internal tooling and feature engineering for time-series forecasting.

Lead product strategy and roadmap for cloud-native retail platforms (POS, AI/Computer Vision, loyalty, promotions). Manage ~20 product professionals, drive migrations, internationalization, compliance, and cross-functional alignment to grow ARR, adoption, and platform scalability.

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

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

Build and operate production-grade model serving infrastructure, design deployment pipelines (blue/green, canary), implement autoscaling and multi-model serving, optimize GPU utilization and network throughput, set up observability and model registries, manage CI/CD for reproducible deployments, own full ML system lifecycle including on-call support and platform scalability.

Build and maintain an AI pipeline that generates structured Markdown from a large legacy codebase using self-hosted LLMs, vector search, and graph queries. Integrate RAG components (Qdrant, Neo4j), add NLI validation, develop prompts/templates, and collaborate with data engineers and QA to meet strict KPIs for coverage and factual precision.

Drive research applying advanced ML (Transformers, GNNs, diffusion models) to semiconductor design and optimization; implement algorithms in Python/C++ using PyTorch; prototype and validate approaches, publish in top conferences, collaborate with engineers, and contribute to EDA tool integration.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

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.

Responsible for delivering AI solutions through the full product lifecycle, collaborating with teams to align technology and business needs, ensuring best practices.

Design, train, and improve computer-vision and generative ML models; handle end-to-end ML pipeline from data collection to deployment; prototype research ideas and convert successful prototypes into product-ready solutions; optimize models for efficient on-device inference and collaborate with product teams to integrate models.

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.

Lead delivery of a multi-agent GenAI PoC for an FMCG/consumer health client: own client relationship, design solution architecture (Discovery, Ideation, Validation, Orchestration), drive evaluation and pilot, manage PoC milestones, present executive-ready materials, and identify Phase 2 opportunities.

Own and deliver ML-powered product features end-to-end: data pipelines, model integration, serving, monitoring, and iteration in production. Productionise research outputs, build ML infrastructure (model serving, inference pipelines, LLM integrations), improve reliability and cost-efficiency, collaborate with product engineers and scientists, and mentor junior engineers.

The role involves designing and scaling MLOps infrastructure on AWS for autonomous driving systems, requiring expertise in machine learning and cloud architecture.

As a Senior Python Engineer, you'll design and implement scalable solutions, collaborate across teams, and manage infrastructure on cloud platforms, ensuring high-quality deliverables.

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.

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.

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

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.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

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.

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