Machine Learning Engineer Team Leader (AI)

TL;DR

Machine Learning Engineer Team Leader (AI): Leading the delivery of high-quality ML scoring solutions for production environments with an accent on deep learning model training, feature engineering, and model lifecycle management. Focus on orchestrating cross-functional execution, driving strategic alignment with product stakeholders, and ensuring scalable performance for business-critical use cases.

Location: Must be based in the European Union. Hybrid work requires on-site workshops in Warsaw 1-3 times per quarter.

Company

hirify.global is a global AI-first digital transformation and engineering partner with over 25 years of experience, specializing in AI, data, cloud, and intelligent automation.

What you will do

• Ensure the team delivers high-quality ML scoring solutions like click and conversion predictions on schedule.
• Manage the training scope for deep learning models, including feature space definition and lifecycle standards.
• Orchestrate the transition of scoring responsibilities in cooperation with backend and product stakeholders.
• Provide expert insight into future roadmaps for the ML scope.
• Maintain responsibility for the team's technical output and project milestones.

Requirements

• Must be based in the European Union and hold a valid work permit.
• 8+ years of experience in Machine Learning or Data Science roles in production environments.
• 2+ years of direct people management experience.
• Hands-on proficiency in Python and SQL with deep learning frameworks like PyTorch or TensorFlow.
• Experience operating ML workloads in cloud environments, preferably GCP.
• Very good command of English, both written and spoken.

Nice to have

• Experience in AdTech, marketplace ranking, or recommendation systems at scale.
• Familiarity with CI/CD for ML, experiment tracking, and feature store integrations.
• Exposure to online inference environments and platform team cooperation.
• Experience developing pCTR/pCVR-like models.

Culture & Benefits

• Strong engineering culture with a consulting mindset.
• Continuous focus on growth and knowledge sharing.
• People-first culture within a global organization.
• Opportunity to work with leading technologies like AWS, Azure, GCP, Databricks, and Snowflake.

Hiring process

• CV review.
• HR call.
• Technical interview.
• Client interview.
• Final decision.