ML Engineer

Role overview

This position is an early-career machine learning engineering role embedded with senior practitioners on internal classical ML initiatives. The work blends structured learning, hands-on delivery, and progressively broader ownership, with the goal of building reliable, reproducible models that move from notebooks into production services.

Responsibilities

- Clean, transform, and validate structured datasets, handling missing values, encoding, scaling, and quality checks before modeling - Run exploratory analyses to surface patterns, anomalies, and candidate modeling approaches - Build, test, and benchmark models across regression, classification, clustering, dimensionality reduction, and anomaly detection - Engineer and select features in collaboration with senior engineers, and tune hyperparameters against chosen validation strategies - Maintain reproducible pipelines, experiment tracking, notebooks, and technical documentation for data and models - Add automated tests for data and modeling code, and help ship models through REST APIs or batch workflows - Communicate assumptions, limitations, and trade-offs of model behavior to peers and stakeholders

Requirements

- At least one year of hands-on Python development with pandas, NumPy, scikit-learn, and Jupyter - Solid foundation in statistics, probability, and linear algebra, plus comfort with exploratory data analysis - Working knowledge of supervised and unsupervised learning and practical experience with the core task families listed above - Skill in preparing structured datasets, including feature engineering and feature selection - Understanding of train/validation/test splits, cross-validation, leakage, overfitting, regularization, and metric selection - SQL ability for data extraction and analysis, plus familiarity with Git, automated testing, Docker, and basic model monitoring - English at B2 level or higher

Nice to have

- Experience with XGBoost or LightGBM, time-series work, recommendation systems, MLflow, and model interpretability techniques - Cloud services, production ML monitoring, and exposure to GenAI, large language models, retrieval-augmented generation, prompt engineering, or agent development

Benefits and work setup

- Technical and non-technical training programs for professional and personal growth - Internal conferences and meetups featuring outside experts - Mentorship pairing with an experienced engineer - Health insurance coverage - Sports and wellness activities - Remote and hybrid work options - Employee referral program - Work-anniversary recognition and additional vacation days