ML Engineer, Recommender Systems (Cloud, A/B Testing)

EPAM Systems; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

EPAM Systems seeks a proactive AI/ML Engineer to design, build, and optimize recommendation and ranking systems at scale in a modern cloud environment. You will develop models delivering personalized experiences and rigorously evaluate their performance.

Collaborate with cross-functional teams to deploy production ML pipelines using Python, SQL, Spark, and ML libraries (scikit-learn, PyTorch, LightGBM/XGBoost) on Databricks, with Git version control and MLflow for experiment tracking.

Qualifications

• 3+ years hands-on experience building recommendation or ranking models.
• Strong Python and SQL skills.
• Experience with Spark and large-scale data processing.
• Familiarity with ranking metrics like NDCG and recall@k.
• Experience with ML tooling (Databricks, MLflow, Git).

Responsibilities

• Design and build learning-to-rank and neural rankers for recommendations.
• Optimize models for multiple objectives using Pareto-front analysis.
• Conduct offline evaluation with ranking metrics like NDCG and recall@k.
• Address bias in logged data to ensure robust performance.
• Track experiments and manage models through their lifecycles.
• Deploy ML workflows on a cloud data platform and collaborate across teams.
• Maintain clean, version-controlled code using Git.

Skills

Python

SQL

Spark

Pandas

Polars

Git

MLflow

Databricks

PyTorch

LightGBM/XGBoost

Tools

Spark

Pandas

Polars

scikit-learn

PyTorch

LightGBM/XGBoost

MLflow

Databricks

Git

Job description

EPAM Systems seeks a proactive AI/ML Engineer to design, build, and optimize recommendation and ranking systems at scale in a modern cloud environment. You will develop models delivering personalized experiences and rigorously evaluate their performance.

Collaborate with cross-functional teams to deploy production ML pipelines using Python, SQL, Spark, and ML libraries (scikit-learn, PyTorch, LightGBM/XGBoost) on Databricks, with Git version control and MLflow for experiment tracking.