ML Engineer

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

We are hiring a Machine Learning Engineer to build production ranking, recommendation, and personalization systems: the models that decide which options a user is shown, in what order, and why. This is a classical ML role at its core, covering feature engineering, gradient-boosted and linear models, learning-to-rank, retrieval and search relevance, and the classification and propensity models that sit alongside them. You will own problems end to end, from framing an ambiguous product question as an ML problem through to a deployed, monitored model, and success will be measured on demonstrated online impact rather than offline metrics alone.

Key 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.

Required Qualifications

• 10+ years of machine learning experience, with a track record of owning models that ran in production and served real traffic.
• Demonstrated production experience with recommendation, ranking, personalization, or search relevance systems, including responsibility for the quality of the ranked output.
• Deep classical ML skills: feature engineering, gradient-boosted trees (e.g., XGBoost, LightGBM), linear and logistic models, tree ensembles, learning-to-rank, regularization, and handling imbalanced data.
• Strong retrieval and search fundamentals, including lexical retrieval (e.g., 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.

Preferred Qualifications

• Experience with two-sided marketplace or matching problems, including supply constraints, capacity awareness, and multi-objective ranking where user relevance and commercial objectives must be balanced.
• Experience with Bayesian and exploration techniques such as bandits, and with counterfactual or off-policy evaluation.
• Experience with real-time or streaming feature infrastructure and feature stores.
• Experience with fairness, transparency, or auditability requirements on ranked and recommended output.
• Experience combining classical ML with LLM components, for example using models for scoring and ranking within an AI-driven or conversational product surface.
• Advanced degree in Computer Science, Machine Learning, Statistics, Operations Research, or a related quantitative field.