Senior Machine Learning Engineer

DXS - Direct Expansion Solutions

. Design, train, and evaluate models for prediction, ranking, and recommendation problems in order management .

Posted 10/2/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in applied machine learning and data science, with a strong foundation in Python, model deployment, and production monitoring. Proficient in translating product requirements into modeling problems and communicating effectively with cross-functional teams.

Highest-signal resume keywords

Applied Machine LearningPython ProgrammingModel DeploymentSQL ProficiencyData Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData ScienceModel EvaluationFeature EngineeringStatisticsSQLPythonPyTorchTensorFlowScikit-learn

Soft Skills

Clear CommunicationCustomer InteractionTeam Collaboration

Tools & Technologies

PandasNumPyTypeScriptJavaScriptSAP

Industry Keywords

Data PreparationModel MonitoringResponsible AIBias EvaluationExperimental Design

Tech Stack

Tools & technologies

JavaScriptNumpyPandasPythonPyTorchScikit-LearnSQLTensorflowTypeScript

About the role

Key responsibilities & impact

Design, train, and evaluate models for prediction, ranking, and recommendation problems in order management
Contribute across problem framing, data preparation, feature engineering, training, evaluation, and retraining strategy
Translate product roadmap priorities into well-framed modeling problems with product management
Profile customer data, tune and validate models, and work with implementation and solution engineering teams
Build reproducible Python training pipelines and experiment tracking
Profile, clean, and validate large-scale enterprise SAP data
Package models and pipelines for deployment
Define production monitoring, including drift detection, performance regression signals, and data quality checks
Translate model behavior and operational reality when production issues arise
Integrate model output into the product stack through documented service interfaces
Apply responsible AI practices, including bias evaluation, explainability, and careful data handling
Document models and explain behavior, limitations, and trade-offs to engineers, delivery teams, sales, and customers
Review peers' modeling work and raise standards for rigor and reproducibility

Requirements

What you’ll need

Demonstrated depth in applied machine learning and data science, with models built that reached production and were measured there
Strong Python skills and fluency with PyTorch or TensorFlow, scikit-learn, pandas, and NumPy
Solid statistics foundation, including experimental design and appropriate evaluation metrics
Strong SQL and comfort working with large relational datasets
Experience building scheduled data and feature pipelines that work with messy source systems
Experience packaging, documenting, and specifying model requirements for a separate operations or platform team
Comfort working directly with customers and delivery teams
TypeScript or JavaScript proficiency sufficient to integrate with the product stack
Clear written and verbal communication
Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related technical field, or equivalent practical experience
Resume must be uploaded in English

Benefits

Comp & perks

Full-time contractor engagement
Equal-opportunity and affirmative-action employer
Equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status