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Machine Learning Engineer Part Time Jobs in Dallas, TX

Experience in applied AI, machine learning, or software engineering with AI components . * Ability to translate AI concepts into working prototypes or production-ready solutions . Required Soft ...

Senior Software Engineer Job Type: Contractor (~15 hrs a week) Location: Remote Job Summary: In ... Familiarity with modern AI or machine learning systems is a plus, though not required. * Background ...

Senior Software Engineer Job Type: Contractor (~15 hrs a week) Location: Remote Job Summary: In ... Familiarity with modern AI or machine learning systems is a plus, though not required. * Background ...

Senior Software Engineer Job Type: Contractor (~15 hrs a week) Location: Remote Job Summary: In ... Familiarity with modern AI or machine learning systems is a plus, though not required. * Background ...

PhD Engineer (Electrical, Mechanical, Chemical) Role Type: Contractor Location: Remote micro1 is ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

PhD Engineer (Electrical, Mechanical, Chemical) Role Type: Contractor Location: Remote micro1 is ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

Domain Expert - (STEM PhD)

Dallas, TX · Remote

$80 - $90/hr

PhD Engineer (Electrical, Mechanical, Chemical) Role Type: Contractor Location: Remote micro1 is ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

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Showing results 1-20

Machine Learning Engineer Part Time information

See Dallas, TX salary details

$31.2K

$127.4K

$191.4K

How much do machine learning engineer part time jobs pay per year?

As of Jul 7, 2026, the average yearly pay for machine learning engineer part time in Dallas, TX is $127,383.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,400.00 and $153,300.00 per year, depending on experience, location, and employer.

How do part-time Machine Learning Engineers typically balance project ownership with limited working hours?

Part-time Machine Learning Engineers often focus on well-defined project segments, collaborating closely with full-time team members to ensure alignment and continuity. Clear communication, thorough documentation, and regular check-ins are key to maintaining progress and integrating their contributions seamlessly. While they may not own entire projects, they often take responsibility for specific modules, models, or experiments, and their schedules are usually coordinated to overlap with team meetings or sprints. This structure allows part-time engineers to add significant value while maintaining a manageable workload.

What is the difference between Machine Learning Engineer Part Time vs Data Scientist Part Time?

AspectMachine Learning Engineer Part TimeData Scientist Part Time
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with ML frameworksBachelor's or Master's in CS, Statistics, or related; experience with data analysis
Work EnvironmentTech companies, startups, research labs; project-basedBusiness, finance, healthcare; data analysis and reporting
Employer & Industry UsageTech firms, AI startups, R&D departmentsCorporate sectors, consulting firms, research institutions

Machine Learning Engineer Part Time focuses on developing and deploying ML models, while Data Scientist Part Time emphasizes analyzing data to extract insights. Both roles often require similar educational backgrounds and may work in overlapping industries, but their core responsibilities differ. Understanding these distinctions helps job seekers target the right position based on their skills and career goals.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer Part Time, and why are they important?

To thrive as a Machine Learning Engineer Part Time, you need strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and ideally a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, and cloud platforms, as well as experience with version control systems like Git, is typically required. Excellent problem-solving abilities, adaptability, and clear communication are valuable soft skills for collaborating on projects and conveying technical concepts. These skills ensure effective development, deployment, and optimization of machine learning models within the constraints of a part-time role.

What is a Machine Learning Engineer (Part Time)?

A Machine Learning Engineer (Part Time) is a professional who designs, builds, and implements machine learning models and algorithms, but works fewer hours than a full-time employee—often on a flexible or project-based schedule. These engineers collaborate with data scientists and software developers to integrate intelligent systems into products or services. Part-time roles are ideal for those seeking work-life balance, students, or professionals supplementing their income. Responsibilities may include data preprocessing, model training, and deployment, but the scope is typically tailored to fit part-time hours.
What are the most commonly searched types of Machine Learning Engineer jobs in Dallas, TX? The most popular types of Machine Learning Engineer jobs in Dallas, TX are:
What cities near Dallas, TX are hiring for Machine Learning Engineer Part Time jobs? Cities near Dallas, TX with the most Machine Learning Engineer Part Time job openings:
AI Intern - Dallas, TX

Part-time

Posted 8 days ago


Job description

AI Engineer Intern - AI Center of Excellence (CoE)

Location: Plano, Texas, USA
Internship Duration: 6-12 months (12 months preferred)
Company: Black Box
Eligibility: Master's students with at least 6 months remaining before graduation and prior professional experience in applied AI

Company Overview

Black Box Network Services is a leading global communications system integrator specializing in designing, sourcing, implementing, and managing complex technology solutions. As part of our strategic transformation, Black Box is expanding its AI Center of Excellence (CoE) to deliver enterprise-grade AI solutions across multiple business domains.

The AI CoE focuses on building scalable, secure, and production-ready AI systems, establishing best practices for enterprise AI adoption, and integrating AI capabilities into core business platforms.

Role Summary

As an AI Engineer Intern in the AI Center of Excellence (CoE), you will contribute to the design, development, and integration of applied AI solutions using pre-trained Large Language Models (LLMs), traditional machine learning techniques, and deterministic approaches.

This role offers hands-on experience building enterprise-grade Generative AI solutions across backend services, data pipelines, orchestration, and user-facing applications. Working closely with experienced AI engineers, you will contribute to real-world AI use cases integrated with platforms such as ServiceNow, SAP, Salesforce, and Azure services.

This internship is designed to strengthen applied AI engineering skills and prepare candidates for conversion into a full-time AI Engineer role.

Eligibility Requirements

  • Currently pursuing a Master's degree in Engineering or a related field (Computer Science, Artificial Intelligence, Data Science, or similar).
  • Must have at least 6 months remaining to complete the Master's program at the time of joining.
  • Must have a minimum of 2 years of relevant professional experience between Bachelor's and Master's programs.
  • Prior experience must include applied AI / Machine Learning, with hands-on exposure to Generative AI use cases.
  • Available for a full-time, on-site internship for a minimum of 6-12 months (depending on academic program constraints).

Key Responsibilities AI & Generative AI Development

  • Build and integrate AI solutions using pre-trained LLMs for conversational AI, summarization, and enterprise knowledge retrieval.
  • Implement RAG-based architectures connecting LLMs with structured and unstructured enterprise data.
  • Develop and test AI agents, traditional ML models, and deterministic logic for real-world use cases.
  • Contribute to AI orchestration using LangChain and workflow automation using n8n.

Full-Stack & Enterprise Integration

  • Build AI-enabled user interfaces and integrate them with backend services.
  • Develop and maintain backend APIs and services.
  • Integrate AI solutions with enterprise platforms such as ServiceNow, SAP, Salesforce, and Azure services.

Data, Testing & Deployment

  • Build and maintain data pipelines, including preprocessing and quality checks.
  • Support testing, debugging, deployment, and monitoring of AI services on Azure.
  • Document AI workflows, integrations, and solution lifecycle updates.

Learning & Collaboration

  • Collaborate with AI, data, and platform teams to deliver production-ready AI solutions.
  • Continuously learn and apply best practices in Generative AI, RAG patterns, and enterprise AI systems.

Required Technical Skills

  • Programming: Strong working knowledge of Python.
  • Applied AI / GenAI: Hands-on experience building or integrating ML or Generative AI solutions.
  • Generative AI: Practical experience with LLMs, prompt engineering, and/or RAG-based architectures.
  • Backend Development: Experience building APIs using FastAPI, Flask, or Node.js (TypeScript).
  • Frontend Development: Working experience building React-based user interfaces and integrating them with backend APIs.
  • Data Handling: Experience working with structured and unstructured data, including basic preprocessing or ETL.
  • APIs & Cloud: Experience consuming REST APIs and familiarity with cloud platforms (Azure preferred).

Required Prior Professional Experience

  • 2+ years of relevant professional experience between Bachelor's and Master's programs.
  • Experience in applied AI, machine learning, or software engineering with AI components.
  • Ability to translate AI concepts into working prototypes or production-ready solutions.

Required Soft Skills

  • Strong learning mindset, ownership, and clear communication with a structured problem-solving approach.

Preferred Skills / Experience

  • Familiarity with NLP concepts and foundational Generative AI models.
  • Awareness of responsible AI and basic AI governance concepts.
  • Exposure to Microsoft Power Platform or low-code automation tools.

About Black Box

Black Box is a leading technology solutions provider focused on accelerating customer success through innovation, ownership, transparency, and collaboration. With over 2,500 team members across 24 countries, Black Box delivers high-value solutions globally and is a wholly-owned subsidiary of AGC Networks. Black Box is an equal opportunity employer.