ML Engineer: Production Ranking & Personalization

Minfy Technologies; Wilmington (DE); On-site; USD 140,000 - 210,000; Full time

Job summary

Minfy Technologies is seeking a Machine Learning Engineer in the McLean, VA area to own end-to-end ranking, recommendation, and personalization systems. You will frame product questions as ML problems, design feature pipelines, and deploy low-latency models handling real traffic.

Responsibilities include building and productionizing models, performing offline and online evaluation, and collaborating with product and engineering to connect model metrics to business outcomes.

Qualifications

• Experience shipping ML models to production with real traffic.
• Strong Python/SQL and ML framework skills.
• Experience with ranking, search relevance, or recommender systems.

Responsibilities

• Build and productionize ranking and recommendation models.
• Frame product problems into ML tasks with correct labeling and metrics.
• Design feature pipelines over behavioral data with correct timing.
• Develop and tune gradient-boosted and linear models, including learning-to-rank.
• Create multi-stage retrieval architectures with latency considerations.
• Improve search relevance via lexical and embedding methods, re-ranking.
• Build classification and propensity models for demand routing.
• Handle data sparsity, cold start, and bias in ranking systems.
• Own offline evaluation and online experiments with A/B tests.

Skills

Python

SQL

Pandas

scikit-learn

XGBoost/LightGBM

PyTorch/TensorFlow

A/B testing

Spark

MLOps

Bayesian methods

Model deployment

Communication

Education

Master's or PhD in CS/ML

Tools

SageMaker

Git

Job description

Minfy Technologies is seeking a Machine Learning Engineer in the McLean, VA area to own end-to-end ranking, recommendation, and personalization systems. You will frame product questions as ML problems, design feature pipelines, and deploy low-latency models handling real traffic.

Responsibilities include building and productionizing models, performing offline and online evaluation, and collaborating with product and engineering to connect model metrics to business outcomes.