Top Machine Learning Jobs in Poland

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.

Design, build, and maintain end-to-end AI/ML solutions including data pipelines, model development, edge deployments, and integrations. Define camera and imaging setups for computer vision use cases, coordinate installations, monitor model performance, and collaborate with IT, software, and infrastructure teams to ensure scalable, secure, and maintainable solutions.

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.

Lead a small senior data engineering team owning the end-to-end data platform: streaming, warehousing, modelling, pipelines, ML lift, and operational excellence. Drive delivery, coach engineers, review technical designs, enforce data contracts and quality, manage costs (FinOps), and partner with Product and leadership to set roadmaps and risk. Build and grow an emerging ML function.

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.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Design, implement, deploy, and monitor scalable ML pipelines and experiments. Develop production-quality code, CI/CD, containerized deployments, unit tests, and troubleshooting. Support customers and stakeholders with demos, documentation, and sales/operations support. Work with LLMs, AI agents, and advanced statistical/numerical methods.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Build and evolve shared, production-grade AI capabilities for construction software: LLM-based experiences, retrieval pipelines, agents, orchestration, evaluation-driven development, and engineering accelerators. Partner with product teams to integrate reusable patterns, apply reliability and governance guardrails, prototype new approaches, and support other engineers through collaboration, reviews, and knowledge sharing.

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.

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.

Design, develop, deploy, and maintain scalable Machine Learning and Generative AI solutions, integrating LLMs and optimizing ML models using cloud-native technologies.

Develop and ship features for Junie across CLI and model integrations, experiment with model setups and ensembling for cost-quality tradeoffs, build and test autonomous agent behaviors for long-term maintenance, and own projects from research to production while using Junie daily to iterate on quality and UX.

Design and implement large-scale retrieval and ranking architectures for personalized recommendations. Build end-to-end ML systems (data processing, feature engineering, training, deployment, monitoring), run A/B tests and offline evaluations, collaborate with product and engineering teams, and continuously monitor and improve model performance.

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.

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.

Develop and optimize the ML training framework in high-performance C++, implement new ops and layers, debug and tune model performance on Tenstorrent chips, collaborate with compiler and kernel teams, and support integration and productionization of real-world models.

Develop SaaS platform components, participate in Agile activities, debug complex problems, and automate tests to ensure high-quality software.

As a Principal AI/ML Engineer, you will lead AI initiatives, architect ML systems, build PoCs, collaborate with data engineers, and mentor team members, focusing on scalable solutions across various domains.