Getir is the pioneer of ultra-fast delivery. By bringing together great technology with a unique operational model, we were the first to introduce the concept of groceries being delivered to your door in minutes.
Great technology is developed by great people, and thats why were looking for talented technologists to join our team.

What You'll Bring

• Bachelors degree in Computer Science, Computer Engineering, or a related field. A Masters degree is a plus, preferably completed or close to completion.
• 5 years of hands-on experience in machine learning and model deployment.
• Strong proficiency with Python and SQL.
• Experience with machine learning libraries like scikit-learn, xgboost/lightgbm/catboost, PyTorch, and/or TensorFlow.
• Developing and maintaining services using frameworks such as FastAPI.
• Previous experience working with GPS and IoT data, experience in deep learning architectures such as LSTMs and GNNs, hands-on experience in large language models (LLMs), including fine-tuning and RAG implementation, are strong plus.
• Interest in applying research methodologies to practical, business-focused solutions.
• Excellent communication skills with the ability to collaborate effectively within cross-functional teams.

Your Responsibilities

• Lead a diverse team of ML engineers, data scientists, and backend engineers across projects in recommendation
  Set technical direction, review system designs, and mentor the team on best practices in scalable ML infrastructure.
  Demonstrating a clear understanding of the company's strategic goals and how technical solutions align with business outcomes
  
• Work closely with product, engineering, and analytics stakeholders to define and prioritize recommendation goals.
  Align model development with strategic business KPIs, presenting insights and recommendations to leadership.
  Collaborates across teams to align architecture with business goals and scalability needs.
  
• Architect and deploy real-time, personalized recommendation pipelines using hybrid modeling approaches (e.g., Two-Tower, DCNv2, GNNs).
  Build and serve user behavior and product embeddings for use in downstream tasks across app touchpoints.
  Continuously experiment and optimize performance through A/B tests and simulation frameworks.
  
• Develop ranking strategies using learning-to-rank frameworks, real-time features, and feedback loops.
  Improve ranking relevance using LightGBM/XGBoost-based models integrated with real-time feature stores.
  
• Design and operationalize end-to-end machine learning pipelines, from training to online inference, using modern MLOps and orchestration tools.
  Ensure reliability and scalability through containerized services, model monitoring, and continuous integration.
  Implements best practices for reproducibility, versioning, and testing.
  Optimizes models for latency, throughput, and reliability at scale.

Benefits

• Hybrid working model
• Take charge of your own career growth with us through professional development opportunities! We really mean it when we say that upward and sideways mobility are some of our favorite terms.
• Health insurance (family included)
• Meal card
• Competitive salary