Informacje o stanowisku

Sr. Engineering Manager, Machine Learning

The success of our business is greatly and directly influenced by our machine learning team. The team includes research, data science, statistical modeling and machine learning experts. The team’s core objective is to automate decision making and increase its accuracy, by creating cutting edge models.

The Role

Senior Machine Learning Engineering Manager will be responsible for leading the data science, and ML engineering, and similar activities across our products developed in DT Warsaw Technology HUB. This role works closely with other engineers, data scientists and leaders in different locations to find innovative ways to apply emerging technologies to improve business processes and decision making, driving more value for our customers. The ideal candidate will have a strong background in machine learning model development, exploratory and predictive analytics which they can utilize to manage and improve our company’s AI and ML initiatives.

Duties and responsibilities

• Build and manage a world class team of data scientists, and data engineers
• Guide the team to architect an entire AI and ML engineering platform that allows for deployment and scalability of machine learning models
• Build the tools and capabilities for model management and model performance monitoring
• Guide the team to operating services at internet scale with high availability and reliability
• Propose and implement the best engineering and research practices for scaling ML powered features, with a goal to enable the fast iteration of and efficient experimentation with a novel features
• Contribute to and influence the ML infrastructure roadmap based on the feedback from internal stakeholders

Skills and experience

• 5+ years of experience in machine learning
• 2+ years of experience in a leadership role
• Experience and understanding of the entire machine learning pipeline from data ingestion to production
• Experience in machine learning operations, software engineering and architecture
• Experience in a large-scale systems including parallel computing
• Experience architecting and building a platform that supports productionization of ML models
• Experience with MLOps systems
• Strong programming skills in a scientific computing languages (i.e. Python)
• Experience using frameworks for machine learning and data science (i.e. pandas, NumPy) 
• Experience working with ML tools (i.e. Tensorflow, PyTorch)
• Ability to take successful, complex research ideas from experimentation to production
• Excellent written and oral communication skills and the capability to drive cross-functional requirements with product and engineering teams

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