Responsibilities
- Drive full-cycle machine learning projects from concept and prototyping to testing, deployment, and operation at scale.
- Create and implement machine learning pipelines processing hundreds of millions of content events using real-time and batch infrastructure.
- Improve capabilities in natural language processing, multimodal artificial intelligence, and content analysis.
- Develop and assess solutions powered by large language models using advanced prompting, retrieval methods, and model coordination strategies.
- Establish robust evaluation frameworks incorporating curated datasets, precision-recall metrics, offline testing, and live experimentation.
- Collaborate with product and engineering leaders, staff engineers, and data scientists to shape technical direction and planning.
- Guide engineering teams to strengthen machine learning practices and raise overall technical standards.
- Support the integration of AI-driven development tools to boost team efficiency and engineering output.
Work Arrangement
Hybrid
Work Arrangement
Hybrid — London, Stockholm
