Machine Learning Engineer

Role Overview

We are looking for a Machine Learning Engineer who can develop ML solutions end-to-end — from understanding business problems, designing models, implementing and iterating on them, to delivering measurable impact in production environments. This role is not research-focused nor purely engineering-focused. The ideal candidate should be able to translate business scenarios into ML solutions and work through the entire lifecycle to production.

Core Responsibilities

• Understand and translate business requirements into ML problems
• Perform data exploration, data quality checks, and feature engineering
• Build, train, evaluate, and iterate ML models using real-world data
• Deploy models into production (with help from data/ML engineering teams as needed)
• Monitor, maintain, and continuously improve deployed models
• Collaborate cross-functionally with business, product, engineering, and data teams
• Communicate model performance and business impact clearly to non-technical stakeholders

Required Skills

• ML Development: Hands-on experience with supervised/unsupervised learning; applied training & tuning; feature engineering; model evaluation
• Production Experience: Built and deployed ML models used in business decisions or products (not just academic or prototype work)
• Data Skills: Strong SQL; comfortable working with real-world messy data; able to diagnose data quality issues
• Problem-Solving: Able to convert vague business needs into well-defined ML tasks
• Programming: Proficient in Python and major ML libraries (e.g., scikit-learn, XGBoost, PyTorch/TensorFlow optional but nice-to-have)
• Communication: Able to explain technical concepts to non-technical stakeholders and present impact clearly

Preferred Background

Not mandatory, but a plus if the candidate has:

• Delivered ML models that generated measurable business or operational impact
• Experience with model monitoring, A/B testing, drift handling
• Exposure to scaling ML solutions (pipeline automation, MLOps collaboration, etc.)

What We Are Not Looking For

To avoid mismatches, we do not target candidates who mainly specialize in:

• Pure academic ML research without production deployment experience
• Pure data engineering without ownership of model building and impact
• MLOps roles focused on infrastructure but not model design
• Data analysts without hands-on ML model development experience
• LLM application roles without broader ML lifecycle experience