Top Machine Learning Jobs in Warsaw

Leads NN Poland’s AI Enablement Team and Center of Excellence, shaping Data & AI strategy, governance, architecture standards, investment priorities, and an enterprise portfolio of AI initiatives. Evaluates opportunities, drives experimentation and proofs of concept, and enables secure, responsible, production-ready AI adoption. The role manages resources, budgets, capabilities, and cross-functional collaboration across business, technology, security, risk, legal, compliance, and data governance teams.

Develop AI-augmented blockchain risk and threat intelligence systems, including autonomous monitoring and investigation workflows. Lead forensic investigations into crypto theft, fraud, and market manipulation. Research post-quantum cryptography and quantum threats to blockchain security, evaluate migration paths, and collaborate with internal teams, external stakeholders, and academic institutions to deploy emerging security tools.

Supports Account Executives throughout the sales cycle by leading technical discovery, solution design, demonstrations, workshops, proofs of concept, evaluations, and technical validation. Translates engineering, simulation, HPC, AI, and infrastructure requirements into Rescale recommendations, documents opportunities in Salesforce, resolves technical risks, supports negotiations, and ensures effective post-sale handoffs across Northern Europe.

Build and enhance RAG systems, AI components, backend services, data pipelines, and REST APIs under senior guidance. Write production code, tests, evaluation harnesses, documentation, and runbooks. Support AWS deployments using containers and CI/CD, investigate model failures, and contribute to architecture and client handovers. The role offers increasing ownership of technical components and decisions while working in distributed, customer-facing delivery teams.

Review and annotate images by identifying people, actions, and predefined categories. Follow detailed annotation guidelines, maintain accuracy and consistency, review completed work, and help create high-quality datasets for AI models. The role requires strong attention to detail, independent work, reliability, and EU citizenship with residence in the European Union.

Lead the Agentic Discovery product chapter, initially owning business tooling, dashboards, transparency, and controls for AI-powered commerce experiences. Define strategy, align ML, engineering, design, and go-to-market teams, scale experimentation, launch reliable LLM-powered experiences, and establish metrics tied to usage, conversion, revenue, and engagement. The role will expand to chapter-wide leadership across on-site, offsite, and integrated agentic discovery surfaces.

Provide technical support for Workday’s enterprise platform and AI agent workflows. Responsibilities include incident resolution, root-cause analysis, cloud and ML pipeline troubleshooting, SQL and log analysis, AI conversation and LLM output evaluation, queue triage, SLA management, customer escalations, and collaboration with engineering and data science teams. The role includes weekend on-call rotations and requires translating technical issues into clear customer communications.

Develop, validate, deploy, and maintain machine learning solutions for medical device manufacturing and CAD/CAM systems. Build deep learning and multimodal AI models using computer vision, 3D geometry, meshes, images, and structured data. Integrate models into production systems, conduct performance analysis, maintain ML workflows, and translate research into practical manufacturing applications while collaborating with engineering, manufacturing, and product teams.

Develop and maintain high-load, real-time personalization services for global e-commerce platforms. Improve shopper experiences and business KPIs through applied machine learning, user interaction modeling, personalization and recommender systems, hypothesis validation, and A/B testing. Collaborate with engineering teams and business stakeholders to research, deliver, and communicate personalization capabilities and outcomes.

Design, deploy, and optimize machine learning models and infrastructure using Databricks. Lead end-to-end model development, including data preparation, deployment, monitoring, and production scaling. Collaborate with data scientists, engineers, business stakeholders, and clients to deliver technical solutions. Mentor junior team members and apply advances in machine learning, NLP, computer vision, and MLOps to solve complex business problems.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Own the full enterprise customer journey from kickoff through adoption and ongoing value realization. Build executive and technical stakeholder relationships, lead QBRs and EBRs, translate product performance into business outcomes, manage risks and escalations, provide strategic ecommerce recommendations, maintain success plans, and partner with Account Management on retention and expansion.

Own enterprise customer relationships from kickoff through adoption and ongoing value realization. Lead success plans, QBRs, executive conversations, escalations, stakeholder alignment, training, and strategic recommendations. Translate technical capabilities and performance data into business outcomes such as revenue, conversion, and customer experience. Partner with Account Management to support retention and expansion while improving scalable onboarding and support processes.

Build and evolve reusable AI capabilities for construction software products. Design production-grade LLM, retrieval, tool-use, orchestration, and agentic systems; establish evaluation-driven development and reliability standards; and prototype scalable AI solutions. Collaborate with product, engineering, and business stakeholders, guide other engineers, improve AI-assisted development practices, and support systems from experimentation through production rollout and continuous improvement.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Conduct research across the full machine learning lifecycle, including ideation, model design, implementation, evaluation, training, inference optimization, and deployment. Develop novel machine learning methods and foundation models, establish evaluation protocols, scale training with GPUs or TPUs, and improve research engineering infrastructure for rapid experimentation.

Lead delivery of real-time fraud decisioning services and platform capabilities for international markets. Design and implement backend services and APIs, integrate regional vendors and ML/AI signals, support operations and on-call, and mentor engineers to maintain high-quality, scalable systems.

Leads complex, cross-functional technical programs for studio infrastructure, cloud environments, hardware provisioning, data migrations, software integrations, and Generative AI/ML platform initiatives. Develops technical strategies, manages dependencies, risks, scope, schedules, and Day 1 readiness, while communicating progress and business impact to stakeholders and leadership across globally distributed teams.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Lead end-to-end architecture, implementation, and delivery of Blue Yonder Warehouse Management and related execution solutions. Serve as the primary client contact, manage timelines, budgets, risks, and deliverables, and lead solution teams and integrations. Provide functional and technical guidance, mentor junior staff, align solutions with business objectives, and collaborate with project managers, delivery partners, and stakeholders to deliver successful client outcomes.

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.

Build and maintain production-grade native iOS applications using Swift and SwiftUI, integrating AI features through backend APIs. Design resilient real-time user experiences, optimize performance and resource usage, collect telemetry, and collaborate with ML and backend teams. The role requires shipping consumer-facing App Store applications with reliable networking, graceful degradation, concurrency, and on-device machine learning capabilities.

Own end-to-end talent acquisition and headhunting for Arago’s technical hiring across France, North America, and Israel. Partner with the CEO, CTOs, and engineering leaders to define roles, source and close senior engineers, scientists, and technical leaders across hardware, optical, software, and research. Build proactive sourcing pipelines, improve interview processes and hiring standards, monitor pipeline health and hiring velocity, and communicate progress to leadership.

Own an AI investing product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reliability, security, and scalability. Incorporate KYC, transaction monitoring, limits, privacy, licensing, and regulatory requirements. Apply AI with evaluation, safeguards, human oversight, and failure handling while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.