Senior Machine Learning Engineer for Research Team

Why this role exists:

We are growing our AI/ML team and are looking for a Senior Machine Learning Engineer to take a leading role in building and scaling applied machine learning systems that power personalization and discovery features across our platform. A key initiative is a large-scale recommendation system based on a two-tower architecture, deployed on AWS and serving millions of users. This position is not limited to a single use case. You will also contribute to other machine learning and data science initiatives, helping define best practices and raising the overall ML maturity of the team.

What you’ll drive:

Technical Leadership & Ownership

• Take technical ownership of core components of recommendation and personalization systems (retrieval, ranking, evaluation).
• Design and evolve two-tower / embedding-based retrieval models and downstream rankers.
• Drive architectural and modeling decisions with a strong understanding of trade-offs between model quality, system complexity, latency, and cost.
• Define and promote best practices for ML system design, experimentation, evaluation, and deployment.
• Review ML designs, pipelines, and code with a focus on correctness, maintainability, and production readiness.
• Act as a technical point of reference for ML-related decisions within the team

Hands-on ML Development

• Develop, train, and improve ML models for retrieval and ranking use cases.
• Work with embedding-based deep learning models and classical ML approaches.
• Perform in-depth data analysis, feature exploration, and systematic error analysis.
• Build reproducible experiments and robust offline evaluation pipelines.
• Optimize models for both offline metrics and online business KPIs.
• Design and operate batch and real-time training and inference workflows in a cloud environment, with awareness of scalability and cost trade-offs.

Experimentation & Production

• Design, run, and analyze offline experiments and online A/B tests.
• Own ML components in production, with a strong focus on reliability, observability, and safe iteration.
• Monitor model performance and data quality in production.
• Collaborate on scalable training and serving infrastructure for ML systems.
• Participate in incident analysis related to ML systems and contribute to root-cause analysis and long-term fixes.
• Design ML systems with failure modes in mind, including fallbacks and graceful degradation.

Collaboration & Mentorship

• Work closely with Data Engineering on data pipelines and feature generation.
• Partner with Product and Analytics to translate business goals into clear ML objectives and success metrics.
• Act as a technical mentor for ML engineers, providing guidance on modeling, experimentation, and production ML.
• Provide constructive feedback through code reviews and design discussions, supporting the growth of the team.

What makes you a GR8 fit:

Must-have

• 5+ years of experience in Machine Learning / Applied Data Science.
• Strong Python skills and experience writing production-quality ML code.
• Solid foundation in core ML and data science 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).
• Strong understanding of ML evaluation, experimentation, and applied statistics.
• Experience deploying, operating, and maintaining ML models in production environments.
• Proficiency with Git, Linux, Docker, and standard ML development workflows.
• Practical experience deploying ML systems in a cloud environment (AWS or equivalent).

Nice-to-have

• Direct experience with recommendation systems or search-related problems.
• Experience designing or operating two-tower / dual-encoder architectures.
• Familiarity with ANN methods and large-scale retrieval (e.g., FAISS or equivalents).
• Understanding of common ML production challenges (training–serving skew, data leakage, model drift).
• Experience collaborating on data pipelines and feature engineering at scale.
• Experience leading or mentoring other ML engineers.
• Experience with experiment automation and hyperparameter optimization (Optuna, Ray /Tune, Hyperopt, or cloud-native equivalents).
• Background in high-scale consumer products (content, gaming, media, marketplaces).
• Experience working with CI/CD pipelines for ML workflows (training, evaluation, deployment).

Tech Stack

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