Careem is building the Everything App for the greater Middle East — making it easy to move around, order food and groceries, manage payments, and more. Our purpose is simple: to simplify and improve people’s lives and build an awesome organisation that inspires.
Since 2012, Careem has enabled earnings for over 2.5 million Captains, simplified the lives of more than 70 million customers, and built a platform where the region’s best talent and entrepreneurs thrive. We operate in 70+ cities across 10 countries, from Morocco to Pakistan.
We’re now entering our next chapter — one powered by AI. We’re looking for AI talent: curious problem-solvers who know how to apply AI to build tools, automate workflows, and create real impact. Whether it’s streamlining operations, enhancing customer experience, or reimagining internal systems — we want people who can make Careem work smarter and move faster.
About the team
The Careem Data Science team’s mission is to drive competitive value from data at scale by building AI models to optimize user experiences, decision-making, and operational efficiencies, and lead the region’s AI ecosystem. As one of the tech leaders in this team, you will be at the forefront of fulfilling this mission. You will be working with the top data science talent of the region while innovating on our user experience using GenAI.
What you'll do
- Collaborate in building a long-term vision of how we can rethink GenAI at Careem
- Drive exploratory analysis to understand the ecosystem and user behavior; identify new levers to help move metrics and build models of user behaviors for analysis and product enhancements using GenAI
- Shape and influence models and instrumentation to optimize the product experience and generate insights on new areas of opportunity and new products.
- Provide product leadership by sharing data-based recommendations to communicate the state of business, the root cause of change in metrics, and experimentation results, influencing product and business decisions
- Implement a scalable machine learning GenAI solution that will be used in production on big data.
- Design and run randomized controlled experiments, analyze the resulting data, and communicate results with other teams.
- You will always challenge the status quo and continually investigate new data processing technologies and seek to ensure that we follow the industry best practices.
- Build and deploy retrieval augmented generation systems and other applications of large language models.
- Collaborate with cross-functional teams, including data scientists, product managers, and domain experts, to deliver AI-driven solutions.
What you'll need
- 4-6 years of experience in machine learning, software engineering, Big Data methodologies, transformation, and cleaning of both structured and unstructured data.
- Advanced degree in a quantitative discipline such as Physics, Statistics, Mathematics, Engineerin,g or Computer Science.
- Strong understanding of transformer architectures, attention mechanisms, and recent advancements in Large Language Models (LLMs)
- Experience with advanced prompting techniques, including Chain of Thought (CoT) prompting, in-context learning, and few-shot learning.
- Proficiency in using LangChain and LangChain Expression Language (LCEL) for building complex pipelines and workflows with LLMs.
- Experience in developing observable LLM-powered compound systems through tracing to monitor performance and behavior in production environments.
- Experience with one of the following machine learning frameworks: PyTorch or TensorFlow.
- Knowledge of distributed training frameworks (e.g., DeepSpeed, Megatron-LM) and optimizing model performance using techniques like mixed-precision training, gradient checkpointing, and model parallelism would be advantageous.
- Experience with sequence-to-sequence models, self-supervised learning techniques, and understanding NLP concepts such as tokenization, parsing, and semantic analysis.
- Proficiency in creating scalable and maintainable APIs using FastAPI or similar frameworks.
- Strong understanding of good software engineering practices, including code versioning (e.g., Git), CI/CD pipelines, and automated testing.
- Experience with both SQL and NoSQL databases for managing training data and model artifacts.
- Proficiency in Python, SQL, and familiarity with data processing frameworks like Spark and Hive.
- Knowledge of classic ML and DL