Machine Learning Engineer

Who WE are?

KAVAK is the number one e-commerce in the purchase and sale of pre-owned vehicles with the fastest growth in Latin America. We work smart and make things happen, offering the easiest, safest and most transparent experience to buy or sell a car.

But our journey doesn't stop there: at KAVAK our goal is global! Now our footprint extends to the vibrant GCC region as part of our expansion into the Middle East, in the pursuit of our one and only goal: to transform the automotive market around the world.

Today we have more than 180 Kavakees on the GCC team and growing, surrounding ourselves with the best talent to continue building the technology, processes, and products that make us the number one player in the pre-owned car market.

Wanna join us on this exciting ride?

Job Description

Build, run and maintain the ML infrastructure behind Kavak UAE's pricing: automated mapping of market listings to catalog SKUs, pricing algorithm, and other ML models that improve pricing accuracy, strategy and margin. Turn manual, person-dependent processes into reliable systems that are measured and retrained regularly.

Key Responsibilities:

• Build and maintain the listing-to-SKU mapping engine: rules, text matching and classifiers, with confidence thresholds and human review queues.
• Develop an image-based car identification model (make, model, year, generation, and trim) for listings with no usable text.
• Manage the local pricing algorithm, monitor performance, run experiments, improve accuracy and reduce bias, and report results.
• Design backtests, holdout sets and audit samples to measure accuracy, and report it by segment.
• Productionise models as scheduled pipelines with monitoring, retraining and data quality checks.
• Build and maintain data pipelines from the data warehouse, scrapers, the catalog and the backoffice.
• Build supporting models for pricing strategy: depreciation and km curves, price elasticity, days-to-sell and demand.
• Turn reviewer feedback and manual overrides into training data and rules with expiry dates.
• Document models, assumptions and results clearly for non-technical stakeholders.
• Work with the Pricing, Data, Product and global teams on data access, deployment and priorities.

Requirements

Skills & Qualifications:

• 3–5 years in ML engineering or applied data science, with at least two models taken to production.
• Background in e-commerce, marketplaces, automotive, fintech or any pricing/valuation domain.
• Experience with tabular models (pricing, regression, classification) and with text and image data.
• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related field.
• A Master's in Machine Learning or Data Science is preferred.

Technical Skills

• Python ML stack: pandas, scikit-learn, XGBoost/LightGBM, plus PyTorch or TensorFlow.
• SQL and data: strong SQL on BigQuery, Databricks or a similar cloud warehouse.
• Pricing models: regression and quantile regression for price ranges, plus prediction intervals.
• Text matching and entity resolution: normalisation, fuzzy matching, tokenisation and embeddings, used to match listings to catalog SKUs.
• Statistics and evaluation: holdout and backtest design, calibration, confidence intervals and A/B testing.
• MLOps: Docker, scheduled pipelines (Airflow or similar), experiment tracking and model registry (MLflow or Weights & Biases), monitoring and retraining.
• Data quality: automated validation of incoming data (Great Expectations or similar).
• Engineering practice: Git on GitLab with CI/CD, code review and tests.
• Cloud: GCP or AWS.
• Computer vision: fine-grained image classification, transfer learning and image embeddings. Experience with vision-language models.
• LLMs and vector search: extracting structured fields from listing text with LLMs, and embedding matching with FAISS or a vector database.
• Pricing analytics: price elasticity, and survival analysis for days-to-sell.
• Human-in-the-loop: labelling tools (Label Studio, CVAT) and active learning to cut manual review.
• Model serving: REST APIs with FastAPI.
• Web scraping: Scrapy or Playwright, handling site layout changes and respecting each site's terms of use.
• Reporting: dashboards in Looker Studio, Streamlit or Databricks.

Nice to have

• Google Cloud Professional Machine Learning Engineer, or AWS Certified Machine
• Learning Engineer - Associate. (Preferred)
• DeepLearning.AI specialisations. (Preferred)
• Arabic Language

Do you want to be part of this story? Apply now!