> Markdown version of [/jobs/ext/4680618-machine-learning-engineer-remote](https://www.wearedevelopers.com/jobs/ext/4680618-machine-learning-engineer-remote). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer - REMOTE

- **Company:** PTP
- **Location:** Austin, TX, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Computer Programming, Customer Data Management, Python (Programming Language), Machine Learning, Natural Language Processing, NoSQL, Application Data, SQL Databases, Systems Integration, Tableau (Software), Model Validation, Information Technology, Text Analysis
- **Published:** November 12, 2026
- **Apply:** https://www.careerbuilder.com/job-details/machine-learning-engineer-remote-austin-tx--89c14f0f-53f8-4c83-9c99-b35aa825a9f8

## About the Role

Machine Learning, System Integration (SI), Customer Experience, Machine Learning, Customer/Client Research, Analysis Skills, Algorithms, Model Validation, Process Analysis, Performance Analysis, * 2+ years experience with:

 + Natural language processing (NLP)
 + Free-text analytics
 + Machine learning (ML) algorithms
 + Predictive modeling and analysis
 + Data visualization software (Tableau preferred)
 * Familiarity with AWS SageMaker
 * Strong programming skills in Python
 * Experience with SQL and NoSQL databases
 * Strong problem-solving ability
 * Excellent written and verbal communication and skills
 * Degree in Computer Science or related field

## Description

* Use machine learning techniques to create scalable solutions for business problems
 * Analyze and extract key insights from rich stores of customer data
 * Research and implement ML algorithms for a variety of business problems
 * Automate processes for analyses, model development, validation, and implementation
 * General application data and performance analysis