ML Engineer: Production Ranking & Personalization

Minfy Technologies; United States; On-site; USD 180,000 - 250,000; Full time

Job summary

Minfy Technologies is seeking an experienced Machine Learning Engineer to build production ranking, recommendation, and personalization systems that decide which options to show users, in what order, and why. You will own problems end to end, from framing product questions as ML problems to deploying monitored models, with success measured by online impact.

You will design feature pipelines, develop classical ML models, and implement multi-stage retrieval and ranking architectures within a

Qualifications

• 10+ years of machine learning experience with production models.
• Proven production experience with recommendation, ranking, personalization, or search relevance systems.
• Deep ML skills: feature engineering, gradient-boosted trees, linear and logistic models, learning-to-rank, and handling imbalanced data.
• Strong retrieval and search fundamentals: BM25, embedding-based and approximate nearest-neighbour retrieval, hybrid retrieval, and re-ranking.
• Rigorous approach to evaluation: offline metric design, ranking metrics, calibration, and the judgment to tell a real improvement from noise or a leaked feature.
• Hands-on experience designing and analyzing A/B tests or other online experiments, and using the results to decide what ships.
• Strong proficiency in Python and SQL, with practical command of pandas, scikit-learn, and at least one gradient boosting library; working knowledge of PyTorch or TensorFlow.
• Data engineering capability: building and maintaining pipelines over large datasets, with distributed processing experience (e.g., Spark) or equivalent.
• Experience deploying models behind real-time, latency-sensitive APIs as well as in batch, on AWS (e.g., SageMaker) or a comparable cloud platform, using containerized workflows.
• Practical MLOps experience: experiment tracking, model registry and versioning, monitoring, and automated retraining.
• Working knowledge of Bayesian methods and their application to modelling under uncertainty and sparse data.
• Solid grounding in statistics and probability, including bias, variance, uncertainty, and enough causal reasoning to know when a correlation should not be shipped as a feature.
• Ability to explain model behaviour, trade-offs, and limitations clearly to product and business stakeholders.

Responsibilities

• Build and productionize ranking and recommendation models that select and order the best options for a given user request, balancing relevance to the user against business objectives.
• Frame ambiguous product problems as ML problems: define the target, the labelling strategy, the training data, and the offline metric that credibly predicts online impact.
• Design and build feature pipelines over behavioral, transactional, and content data, with point-in-time correctness so training features match what is available at inference and no label leaks into the model.
• Develop and tune classical ML models (gradient-boosted trees, linear and logistic models, learning-to-rank), and recognize when a simpler model or a rules baseline is the right answer.
• Build multi-stage retrieval and ranking architectures: candidate generation, eligibility and business-rule filtering, scoring, and re-ranking, all within a production latency budget.
• Improve search and match relevance using lexical and embedding-based retrieval, hybrid approaches, and re-ranking, with query understanding where it moves the metric.
• Build classification, segmentation, and propensity models used to score and route incoming demand.
• Handle the constraints real ranking systems have: sparse and skewed data, cold start for new users and new inventory, position and selection bias, feedback loops, and eligibility or capacity limits on what can be recommended.
• Own offline evaluation: temporally honest splits, ranking metrics such as NDCG, MAP, and recall@k, calibration, and slice-level analysis to find where a model fails rather than reporting a single average.
• Design and analyze online experiments, including A/B test design, power analysis, guardrail metrics, and clear-eyed interpretation of results, including negative ones.
• Deploy models to production as low-latency inference services or batch scoring jobs, with versioning, reproducibility, and safe rollout.
• Monitor production models for drift, degradation, and upstream data quality issues, and own the retraining and refresh cycle.
• Partner with product and engineering to connect model metrics to business outcomes, and document modelling decisions, assumptions, and trade-offs so results can be reproduced and challenged.

Skills

Python

SQL

Pandas

scikit-learn

XGBoost/LightGBM

Learning-to-Rank

A/B testing / online experiments

Distributed data processing

ML deployment / MLOps

Bayesian methods / uncertainty

Education

Advanced degree in Computer Science / ML / Statistics

Tools

Spark

SageMaker / cloud ML platforms

PyTorch / TensorFlow

Job description

Minfy Technologies is seeking an experienced Machine Learning Engineer to build production ranking, recommendation, and personalization systems that decide which options to show users, in what order, and why. You will own problems end to end, from framing product questions as ML problems to deploying monitored models, with success measured by online impact.

You will design feature pipelines, develop classical ML models, and implement multi-stage retrieval and ranking architectures within a