Middle Machine Learning Engineer for Research team

Why this role exists:

We are expanding our AI/ML team and are looking for a Machine Learning Engineer to help us scale applied ML systems that power personalization and discovery across our platform. You will play a key role in developing a large-scale recommendation system based on a two-tower architecture, deployed on AWS and serving millions of users. This is an opportunity to move beyond theory and build production-grade systems alongside senior experts, contributing to a variety of ML and data science initiatives.

What you’ll drive:

ML Development & Implementation

• Develop, train, and iterate on ML models for retrieval and ranking use cases.
• Work with embedding-based deep learning models and classical ML approaches.
• Perform data analysis, feature exploration, and systematic error analysis to improve model performance.
• Build and maintain reproducible experiments and robust offline evaluation pipelines.
• Optimize models for both offline metrics and online business KPIs.

Production & Operations

• Support and improve ML components in production, focusing on reliability and observability.
• Design and operate batch and real-time training and inference workflows in a cloud environment.
• Monitor model performance and data quality to detect drift or degradation.
• Collaborate on scalable training and serving infrastructure to ensure low-latency performance.
• Participate in incident analysis and contribute to long-term fixes for ML systems.

Experimentation & Collaboration

• Assist in designing, running, and analyzing offline experiments and online A/B tests.
• Work closely with Data Engineering to build efficient data pipelines and feature sets.
• Participate in design reviews and code reviews to ensure maintainability and production readiness.
• Partner with Product and Analytics to understand business goals and translate them into technical ML tasks.

What makes you a GR8 fit:

Must-have

• 3+ years of professional experience in Machine Learning or Applied Data Science.
• Strong Python skills and experience writing clean, production-quality code.
• Solid foundation in core ML tools: NumPy, Pandas, scikit-learn, etc.
• Hands-on experience with deep learning frameworks (PyTorch or TensorFlow).
• Practical experience with embedding models and similarity-based retrieval.
• Experience with tree-based models (LightGBM, XGBoost).
• Clear understanding of ML evaluation metrics, experimentation, and applied statistics.
• Experience working with Git, Linux, Docker, and standard development workflows.

Nice-to-have

• Experience with recommendation systems or search-related problems.
• Familiarity with two-tower / dual-encoder architectures.
• Knowledge of ANN methods and large-scale retrieval (e.g., FAISS).
• Understanding of common ML production challenges (training–serving skew, data leakage, model drift).
• Practical experience with cloud-native ML tools (e.g., AWS SageMaker).
• Experience with experiment automation or hyperparameter optimization (Optuna, Ray Tune).

Tech Stack:

• Languages: Python, SQL.
• Core ML / DS: NumPy, Pandas, scikit-learn.
• Deep Learning: PyTorch / TensorFlow.
• Models: LightGBM, XGBoost, Two-Tower.
• Cloud & Data: AWS, S3, Glue, SageMaker.
• Dev & MLOps: Git, Docker, Linux.