Machine Learning Engineer

New

Remote for US based candidates onlyFull-TimeMiddle

Salary120,000 - 140,000 USD per year

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Job Details

Experience: 4 or more years of experience building and deploying machine learning models in production environments
Required Skills: PythonMachine LearningPandasscikit-learn

Have 4 or more years of experience building and deploying machine learning models in production environments.
Have strong Python skills.
Have experience with pandas, scikit-learn, or similar machine learning and data frameworks.
Have experience with time-series forecasting, weighted scoring, ensemble models, optimization, recommendation, or risk-scoring models.
Have experience applying quantitative models to enterprise business problems.
Be comfortable working with messy real-world data, including missing values, inconsistent history, and unreliable third-party inputs.
Have experience building explainable models or decision logic for human-reviewed, regulated, or audited workflows.
Be familiar with integrating machine learning outputs into downstream business systems through APIs, event-driven pipelines, or similar methods.
Be able to explain model behavior to non-technical stakeholders.
Have a bachelor's or master's degree in Data Science, Statistics, Computer Science, Engineering, Mathematics, or a related field.

Design and build standalone quantitative models for forecasting, optimization, scoring, pattern detection, and relationship analysis.
Develop model components with defined inputs, outputs, assumptions, performance expectations, and testing criteria.
Build and maintain data pipelines combining historical data, vendor inputs, market signals, trend data, and other enterprise sources.
Prepare model-ready features from incomplete, inconsistent, or unreliable data.
Develop backtesting and evaluation frameworks to compare model outputs with historical outcomes.
Validate model performance, accuracy, reliability, confidence ranges, and limitations.
Monitor model drift, input quality, accuracy changes, and data-source reliability; define retraining, reweighting, or escalation triggers.
Register models with versioning, explainability, and audit support, and document model interfaces and behavior.
Collaborate with business analysts, AI platform teams, and governance teams to integrate models with downstream systems.
Support user acceptance testing by explaining model outputs and tradeoffs to stakeholders.

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