Middle Machine Learning Engineer

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.