Top Machine Learning Jobs in Warsaw

Lead strategy and delivery of enterprise AI and intelligent automation programs. Identify high-value use cases, design and deploy RPA and AI/ML solutions, build automation roadmaps and governance, drive cost savings and ROI, integrate automation into transformation programs, and mentor teams while advising senior stakeholders and clients.

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.

Develop predictive models and analytical solutions to support commercial growth. Translate business problems into data-driven approaches, partner with cross-functional stakeholders, analyze customer behavior and market trends, produce dashboards and visualizations, ensure data quality, and communicate actionable recommendations to technical and non-technical audiences.

Write, edit, localize and proofread English-language email and social media copy for Google Nordics/Benelux; create social-first assets, run A/B test variants, collaborate with designers, developers, analytics and audience teams, and maintain brand voice and campaign documentation.

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.

Support integration of AI and automation across the data platform, build ETL and data quality checks using Dataiku, prepare data for reporting and analysis, improve reporting via ML and automation, collaborate with analysts and stakeholders to deliver reliable AI/data solutions and identify data improvement opportunities.

Lead end-to-end delivery and validation of AI/analytics products in a regulated pharma environment. Drive solution validation (GxP/CSV), remove delivery bottlenecks, align cross-functional teams, mentor junior delivery managers, and liaise with senior stakeholders to ensure compliant, high-impact product releases.

Lead investigations into KPI shifts, mentor Brand Analysts, bridge business and data science teams, and create innovative segmentation strategies.

Lead and build Solidgate's ML/AI function from scratch: define vision and architecture, hire and lead the team, deliver real-time decisioning and recommendation systems, partner with Product/Engineering/Data Platform, run experiments and A/B tests, and translate models into revenue-driving production features.

The Data Scientist will design advanced algorithms for generative AI models, analyze large datasets, and collaborate on AI advancements to enhance business processes.

Drive end-to-end product development for iTero 3D imaging systems by translating clinical needs into software requirements, leading cross-functional agile teams, driving ML diagnostic initiatives from PoC to productization, analyzing clinical usage data for improvements, and embedding customer feedback into product decisions to enhance usability, performance, and clinical value.

Lead positioning, messaging, and go-to-market for FineReader and ABBYY's Document Parser/SDK. Drive developer ecosystem strategy, technical content, enablement, and GTM alignment while gathering market intelligence to inform product and growth priorities.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

The Senior Cybersecurity Engineer will innovate IAM processes through automation and AI, ensure compliance, and collaborate with teams to enhance security.

You will design and optimize LLM-based machine learning solutions, focusing on natural language processing, deep learning methods, and inferencing pipelines.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Design, automate, and maintain credit risk data, ETL and reporting processes. Build Power BI dashboards, automate workflows with Microsoft Power Platform, write SQL/Python queries, partner with risk managers, and apply ML/AI to improve reporting and decision-making.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

As an Engagement Manager, you'll ensure customer success in protein R&D by building relationships, tracking metrics, and leading cross-functional teams. You'll manage enterprise accounts and advocate for customers while aligning their needs with Cradle's offerings.

As a Senior Product Manager, you will define product vision, analyze market trends, manage product KPIs, and collaborate with teams to deliver innovative solutions.

The ML Ops/Data Engineer will ensure reliability and scalability of ML systems, manage data pipelines, monitor performance, and collaborate with teams for optimization.

Analyze large datasets and apply statistical and ML techniques to measure and improve platform performance and reliability. Build diagnostic tools, define KPIs, mine internal data feeds, automate analytical workflows, and partner with engineering, operations, and product teams to influence technical decisions and increase system reliability.

Lead development and productionization of TTS and voice-generation systems: train, fine-tune, evaluate models, deploy and optimize for inference, latency, scaling, monitoring, and reliability; translate research into production and define technical direction while collaborating with research, engineering, and product.