Machine Learning Engineer
W skrócie
Machine Learning Engineer for a global leader. Focus on AI/ML, predictive analytics, generative AI. Requires Python, ML frameworks (TensorFlow, PyTorch, Scikit-Learn), cloud deployment (AWS, Docker, Kubernetes), and LLM experience. Remote from Warsaw.
Słowa kluczowe
Skrót przygotowany przez AI na podstawie treści ogłoszenia.
Machine Learning Engineer💰 Salary: 5000-9000$ per month (depending on experience)🕦 Full-time☑️ CoE🌴Remote from WarsawWe are looking for a Machine Learning Engineer for a global leader in unified marketing measurement and optimization, recognized by leading industry analysts. The role sits within a NextGen investment initiative, focused on expanding AI/ML capabilities through predictive analytics, generative AI, and agentic systems.
Requirements
Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-Learn, XG
Boost, etc.)
Experience with data pipelines and deploying ML systems in cloud environments (AWS, SageMaker, Docker, Kubernetes)
Hands-on production experience with LLMs and generative AI services (AWS Bedrock, Anthropic Claude, OpenAI, or comparable)
Strong foundations in algorithms, data structures, and software engineering
- Attention to detail with a focus on quality control and versioning
- Nice to Have
- Advanced degree (Master's or Ph.
D.) in CS, ML, applied mathematics, statistics, or related field
- Experience designing automated evaluation, quality monitoring, or self-correction systems for AI/ML applications
- Familiarity with numerical programming and optimization methods
- Background in marketing analytics or communicating technical tradeoffs to non-technical stakeholdersKey Responsibilities
- Design, develop, and test end-to-end machine learning systems
- Implement machine learning and statistical algorithms
- Bridge the gap between data scientists and software engineers — translating analytical specs into production-ready applications
- Build prototypes and MVPs from minimal proofs of concept
- Apply generative AI and agentic approaches to synthetic data generation, model hypothesis development, and automated quality review
| Opublikowana | 2026-08-27 |
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