Top Machine Learning Jobs in Poland

Design, build, and operate scalable MLOps systems and workflows for large-scale distributed ML. Implement observability (Prometheus, Grafana, OpenTelemetry), CI/CD, container orchestration, and cloud infrastructure (AWS/GCP, Terraform). Collaborate with data scientists on LLM/generative AI deployment, review architectures, and mentor junior engineers.

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.

Lead design and productionization of ML pipelines, evaluation systems, and AI agents for cybersecurity. Own experimentation-to-production lifecycles, integrate ML across product teams, scale ML infrastructure, optimize agent performance, and mentor junior engineers while collaborating with CTO and product leadership.

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.

Design and optimize ML models for perception, prediction, and planning in automotive parking systems. Build computer-vision and multi-sensor processing solutions, trajectory forecasting, training/evaluation pipelines, analyze large datasets, validate models in real-world scenarios, and collaborate with software, systems, and product teams to improve algorithms and tools.

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.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

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.

Design and deploy end-to-end document intelligence pipelines on AWS. Develop and optimize models for document classification, segmentation, and field extraction; build scalable data processing for large PDFs; own features from research to production; establish evaluation frameworks and monitor extraction quality.

Lead engineering of an AI-assisted pipeline to generate structured Markdown documentation from a large legacy codebase. Build and optimize self-hosted LLM inference pipelines (Llama/Mixtral), implement NLI-based validation and RAG using Neo4j and Qdrant, tune inference performance, develop prompts and fine-tuning, collaborate on data schemas, and support automated testing to meet strict KPI targets for coverage, factual precision, and SME acceptance.

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.

Build and scale ML-powered search and RAG capabilities at Zendesk scale. Collaborate with product, ML scientists, and engineers to design, deploy, and maintain reliable production services, improve relevance and ranking, optimize indexing pipelines, and mentor teammates while applying experiments and analysis to drive decisions.

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, 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.

Lead the AI/ML core for a robotics automation startup: design, train, and fine-tune models, build computer vision pipelines, integrate AI with hardware, define the AI stack, mentor engineers, and translate customer feedback into technical requirements.

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.