ML Engineer Mid

Experience and Skills

• 3+ years of experience in machine learning engineering or data science roles
• Strong Python proficiency with scikit-learn, pandas, and numpy
• Hands-on experience building recommendation systems using collaborative filtering, content-based filtering, or matrix factorization
• Proven track record solving sparse data modeling and cold-start problems in production or research settings
• Ability to design and execute ML experiments independently with rigorous evaluation methods

Skills

• Python
• Machine Learning
• Recommendation Systems
• scikit-learn
• pandas
• numpy
• Collaborative Filtering
• Sparse Data Modeling
• Cold-Start Problem
• Experimental Design
• ML Model Evaluation

Projects

Join a small, focused team building a recommendation engine for small and medium businesses with limited transactional data. You will own the design and implementation of ML solutions that handle sparse data and cold-start problems simultaneously. This is a 6-month R&D project with real continuity potential, working directly with a technical lead on production systems that impact 150+ active merchants.

Key Responsibilities

• Design and implement a recommendation engine that suggests optimal loyalty strategies per merchant based on industry vertical, program metrics, and customer behavior
• Build ML pipelines that perform reliably with small, imbalanced datasets from 150+ active merchant locations
• Run autonomous R&D experiments to validate approaches for sparse data and cold-start scenarios
• Document methodologies, model performance, and decision rationale for technical handoff

Nice to Have

• PyTorch
• TensorFlow
• MLOps
• Transfer Learning
• Few-Shot Learning
• Matrix Factorization
• Content-Based Filtering
• Bayesian Methods
• LLMs
• Model Versioning
• SQL