Middle Machine Learning Engineer

WorldwideRemoteMiddle

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).