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Machine Learning Engineer
Descrizione del lavoro
A technology-driven organization in the UK is seeking a Machine Learning Engineer to build algorithms for speaker-event matching and event recommendations. Responsibilities include optimizing data pipelines and managing data collection processes. Ideal candidates will have proficiency in Python and experience with machine learning frameworks like TensorFlow and PyTorch.
Competenze
- Proficiency in Python and experience with machine learning frameworks.
- Experience with vector databases and recommendation systems.
- Strong analytical skills.
- Ability to manage projects independently.
Mansioni
- Build and maintain ML algorithms for speaker-event matching.
- Develop data pipelines and vector database integrations.
- Manage data collection processes for model accuracy.
- Analyze business requirements for scalable ML solutions.
Conoscenze
Strumenti
World Salon is looking for a Machine Learning Engineer to build recommendation systems and automation models. The responsibility will be developing algorithms and data models to match speakers with suitable events and recommend events to users interests. The candidate will contribute to data collection strategies, optimize existing datasets, and implement machine learning models using Python and vector databases.
- Build and maintain ML algorithms for speaker-event matching and personalized event recommendations.
- Develop and optimize data pipelines and vector database integrations.
- Manage data collection processes to enhance model accuracy.
- Analyze business requirements and translate them into scalable ML solutions.
- Proficiency in Python and experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn, etc.).
- Experience working with vector databases and recommendation systems.
- Strong analytical skills
- Ability to manage projects independently
* Il benchmark retributivo si basa sugli obiettivi retributivi dei leader del mercato nei rispettivi settori. È pensato per orientare gli utenti Premium nella valutazione delle posizioni aperte e aiutarli a negoziare la propria retribuzione. Tale benchmark non è fornito direttamente dall'azienda, quindi la retribuzione effettiva potrà risultare anche notevolmente superiore o inferiore.