ML Engineer Mid
Niuro · Remote
- 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
- 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
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.
- PyTorch
- TensorFlow
- MLOps
- Transfer Learning
- Few-Shot Learning
- Matrix Factorization
- Content-Based Filtering
- Bayesian Methods
- LLMs
- Model Versioning
- SQL