ZipRecruiter

Log In

1

Temporary Machine Learning Trainer Jobs (NOW HIRING)

1 point system

Machine Learning Engineer

Fort Mill, SC · Remote

$48/hr

Job Title Machine Learning Engineer Location Remote Rate $48/hr on W2 Must Haves: Neaural networks ... They should be comfortable selecting the right algorithms, preparing and analyzing data, training ...

KSB GIW, Inc.

Machine Learning Engineer

Grovetown, GA · On-site

$70 - $110/hr

Machine Learning Engineer Department: Engineering, Research & Development Reports to: Metallurgical ... Foundational understanding of neural networks, model training, and optimization * Experience with ...

New

Flexjet

Machine Learning Engineer

Cleveland, OH

DUTIES & RESPONSIBILITIES * Assist in developing and training machine learning models * Support the creation and maintenance of data pipelines * Help deploy ML models into production under guidance

Hive

Machine Learning Manager

Seattle, WA · On-site

$180K - $250K/yr

We're interested in experimenting with new models, new ideas, and training on novel datasets. Our ideal candidate has experience managing a team of machine learning engineers working on ML projects ...

FloVision Solutions

Machine Learning Engineer

OR · On-site +1

POSITION OVERVIEW As a Machine Learning Engineer at FloVision, you will design, develop, and ... On-site data collection for model training and validation * R&D visits to one of our in-person ...

New

Hive

Machine Learning Manager

San Francisco, CA · On-site

$180K - $250K/yr

We're interested in experimenting with new models, new ideas, and training on novel datasets. Our ideal candidate has experience managing a team of machine learning engineers working on ML projects ...

Spear AI

Machine Learning Engineer

Required : • Several years of experience with Python and machine learning frameworks • Expertise in PyTorch for building and training neural networks • Experience training and serving models in ...

FloVision Solutions

Machine Learning Engineer

On-site data collection for model training and validation * R&D visits to one of our in-person ... Own machine learning outcomes end to end - from data quality and model performance to deployment ...

New

SysMind Tech

Machine Learning

Manhattan, NY · On-site

$85/hr

Title - Machine Learning ( F2F interview is required) Location - New York, NY ( Hybrid 2-3 days ... with ML model training within cloud infra such as Azure, AWS, GCP Proven track record of ...

Hive

Machine Learning Manager

San Francisco, CA

$180K - $250K/yr

We're interested in experimenting with new models, new ideas, and training on novel datasets. Our ideal candidate has experience managing a team of machine learning engineers working on ML projects ...

Paylocity

Machine Learning Engineer

Schaumburg, IL · On-site

Drive the adoption of best practices in machine learning engineering across teams, contributing to the development of formal training programs and materials for MLE tool adoption. * Actively ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

We have an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience ...

Corning

Major responsibilities and tasks of the position: - Participate in the development and maintenance of end-to-end machine learning pipelines, including data ingestion, preprocessing, training ...

Paylocity

Machine Learning Engineer

Machine Learning Engineer Position Overview Paylocity is growing its Machine Learning Engineering ... development of formal training programs and materials for MLE tool adoption. • Actively ...

Machina Labs

Machine Learning Engineer

Chatsworth, CA · On-site

$160K - $190K/yr

Strong experience designing, building, training, and testing machine learning models end-to-end. * Proven ability to work with raw, unstructured, or incomplete data, including data collection ...

Machina Labs

Machine Learning Engineer

Los Angeles, CA

$160K - $190K/yr

Strong experience designing, building, training, and testing machine learning models end-to-end. * Proven ability to work with raw, unstructured, or incomplete data, including data collection ...

KSB, Inc.

Machine Learning Engineer

Grovetown, GA · On-site

$70 - $95/hr

## Machine Learning EngineerApplylocations: Grovetown, GAposted on: Posted Todayjob requisition id: JR ... Foundational understanding of neural networks, model training, and optimization* Experience with ...

previous page

Showing results 41-60

All Jobstmp JobsTechnology JobsData JobsData Scientist JobsTemporary Machine Learning Trainer Jobs

Temporary Machine Learning Trainer information

See salary details

$28K

$87.3K

$112.5K

How much do temporary machine learning trainer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for temporary machine learning trainer in the United States is $87,325.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,000.00 and $111,000.00 per year, depending on experience, location, and employer.

What is a temporary machine learning trainer?

Temporary Machine Learning Trainers are professionals hired on a short-term or contract basis to develop, implement, and refine machine learning models or to train teams in machine learning techniques. Their responsibilities often include preparing training data, selecting appropriate algorithms, and ensuring models are accurate and efficient. They may also provide guidance to organizations on best practices and help upskill employees in machine learning concepts. These roles are typically project-based and may last from a few weeks to several months, depending on organizational needs.

What are some common challenges faced by temporary machine learning trainers, and how can they be managed effectively?

Temporary Machine Learning Trainers often face the challenge of quickly adapting to new team environments and rapidly understanding existing workflows. Additionally, they may need to balance delivering training sessions with handling updates to curriculum or technology. Effective communication with permanent staff and staying up-to-date with the latest machine learning tools can help manage these challenges. Being proactive in seeking feedback and clarifying expectations early on can also contribute to a smoother transition and more impactful training sessions.

What are the key skills and qualifications needed to thrive as a temporary machine learning trainer, and why are they important?

To thrive as a Temporary Machine Learning Trainer, you need a solid background in machine learning concepts, data analysis, and model evaluation, usually supported by a relevant degree or experience in computer science or a related field. Familiarity with programming languages like Python, machine learning libraries (such as TensorFlow or scikit-learn), and educational tools is typically required. Strong communication, adaptability, and instructional skills help trainers effectively convey complex topics and respond to diverse learner needs. These skills ensure trainees gain practical knowledge and confidence, contributing to successful training outcomes and organizational goals.

What is the difference between Temporary Machine Learning Trainer vs Data Scientist?

AspectTemporary Machine Learning TrainerData Scientist
CredentialsRelevant certifications (e.g., AWS, Google Cloud), technical trainingAdvanced degrees (Master's or PhD) in data science, statistics, or related fields
Work EnvironmentTraining sessions, workshops, corporate training settingsData analysis, modeling, research environments, often in offices or labs
Employer & Industry UsageTech companies, educational institutions, consulting firmsTech, finance, healthcare, research organizations

While both roles involve working with data and machine learning, a Temporary Machine Learning Trainer primarily focuses on educating and training teams or clients on machine learning tools and concepts. In contrast, a Data Scientist develops models, analyzes data, and derives insights for decision-making. The roles differ mainly in their focus—training versus data analysis—though they share foundational technical skills.

What cities are hiring for Temporary Machine Learning Trainer jobs?

Cities with the most Temporary Machine Learning Trainer job openings:

What are the most commonly searched types of Machine Learning Trainer jobs?

The most popular types of Machine Learning Trainer jobs are:

What states have the most Temporary Machine Learning Trainer jobs?

States with the most job openings for Temporary Machine Learning Trainer jobs include:

Temporary Machine Learning Trainer jobs near you
Infographic showing various Temporary Machine Learning Trainer job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 72% Full Time, 25% Part Time, 1% Temporary, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $87,325 per year, or $42 per hour.

Machine Learning Engineer

1 point system

Fort Mill, SC • Remote

$48/hr

Contractor

Posted 25 days ago

Job description

Hi ,
I hope you're doing well.

I'm reaching out regarding an exciting opportunity that I believe aligns well with your background and skill set.

To move forward, could you please provide the following details along with latest copy of resume:

Work Authorization and Expiry (If any)

LinkedIn Profile URL

Current Location with Zip code

Pay Expectation on W2 (hourly)

Complete JD:

Job Title

Machine Learning Engineer

Location

Remote

Rate

$48/hr on W2

Must Haves:
Neaural networks
NLP
Python
AZURE
Pytorch or tensorflow
Job Description:
Machine Learning Engineer / AI Engineer Role

Role Overview

This role is focused on developing, deploying, and optimizing machine learning models for enterprise applications. The ideal candidate should have strong hands-on experience with machine learning algorithms, neural networks, NLP, Python/R/SQL, modern ML frameworks, Microsoft Azure, and DevOps/MLOps practices. This is not just a data science research role — the candidate needs to be able to build models and support deployment/management in a production environment.

Must-Have Skills

The candidate must have hands-on experience with:

  • Supervised and/or unsupervised machine learning algorithms
  • Neural networks
  • Natural Language Processing, NLP
  • Python
  • R
  • SQL
  • TensorFlow, Keras, and/or PyTorch
  • Microsoft Azure cloud platform
  • DevOps and/or MLOps practices
  • Model development, deployment, optimization, and lifecycle management

Strong Fit Profile

A strong candidate will have experience building and deploying machine learning models from end to end. They should be comfortable selecting the right algorithms, preparing and analyzing data, training models, evaluating performance, and deploying models into cloud-based environments.

They should also understand MLOps concepts such as CI/CD for ML models, version control, monitoring, automation, model retraining, and production support. Azure experience is important, especially if they have used Azure Machine Learning, Azure DevOps, Azure Databricks, Azure Functions, or related cloud services.

Key Screening Questions

Machine Learning Experience

  1. Can you walk me through a machine learning model you developed from start to finish?
  2. What supervised learning algorithms have you worked with most often?
  3. What unsupervised learning algorithms have you used, and what business problems were they solving?
  4. How do you determine which algorithm is the best fit for a use case?
  5. How do you evaluate model performance and accuracy?

Neural Networks / NLP

  1. What experience do you have building or working with neural networks?
  2. Have you worked on any NLP-related projects? If so, what was the use case?
  3. What NLP techniques, libraries, or models have you used?
  4. Have you worked with text classification, sentiment analysis, entity extraction, chatbots, or language models?
  5. How do you clean and prepare text data for NLP models?

Tools / Programming Languages

  1. How strong would you rate your Python skills?
  2. Have you used R in a professional setting? If yes, for what type of work?
  3. How have you used SQL in your machine learning or data science work?
  4. Which ML frameworks have you used: TensorFlow, Keras, PyTorch?
  5. Which framework are you strongest in, and why?

Azure / Cloud Experience

  1. What Microsoft Azure services have you used for machine learning or data work?
  2. Have you used Azure Machine Learning before?
  3. Have you deployed ML models into Azure environments?
  4. Have you worked with Azure DevOps, Azure Databricks, Azure Functions, or Azure Pipelines?
  5. Can you describe a cloud-based ML project you supported?

DevOps / MLOps

  1. What does MLOps mean in your previous experience?
  2. Have you built or supported CI/CD pipelines for machine learning models?
  3. How have you handled model versioning, monitoring, or retraining?
  4. Have you worked with containerization tools like Docker or Kubernetes?
  5. How do you manage models once they are in production?

Deployment / Optimization

  1. Have you deployed machine learning models into production?
  2. What challenges have you faced during model deployment?
  3. How do you monitor model performance after deployment?
  4. Have you optimized models for performance, scalability, or accuracy?
  5. What steps do you take when a model’s performance starts to decline?

Candidate Must Be Able to Explain

The recruiter should listen for examples where the candidate can clearly explain:

  • What business problem they were solving
  • What data they used
  • What algorithm or model they selected
  • Why they selected that approach
  • What tools/frameworks they used
  • How they measured success
  • How the model was deployed
  • How the model was monitored or maintained
  • Their exact role in the project

Green Flags

Strong candidates may mention experience with:

  • Azure Machine Learning
  • Azure DevOps
  • Azure Databricks
  • CI/CD pipelines
  • Model monitoring
  • Model retraining
  • Model versioning
  • Feature engineering
  • NLP pipelines
  • Text classification
  • Neural network architecture
  • TensorFlow, Keras, or PyTorch in production
  • Python-heavy ML development
  • SQL for data extraction and analysis
  • End-to-end model deployment
  • Production ML environments
  • MLOps lifecycle ownership

Red Flags

Watch out for candidates who:

  • Only have academic or theoretical ML experience
  • Cannot explain specific models they have built
  • Have used Python only for scripting, not ML development
  • Have no Azure experience
  • Have no production deployment experience
  • Only know ML frameworks at a high level
  • Have no DevOps or MLOps exposure
  • Cannot explain supervised vs. unsupervised learning
  • Have only used pre-built tools without understanding the models
  • Cannot describe how they monitored or optimized a model after deployment

Quick Recruiter Intake Notes

Top priority: ML model development + deployment

Cloud requirement: Microsoft Azure

Programming must-haves: Python, R, SQL

Frameworks: TensorFlow, Keras, PyTorch

AI/ML focus: Supervised learning, unsupervised learning, neural networks, NLP

Operational focus: DevOps/MLOps, model deployment, monitoring, optimization

Best candidates: Hands-on ML engineers or data scientists with production deployment experience

Avoid: Candidates who only have academic ML exposure or no Azure/MLOps experience

Thank You

Ranjeet Kumar | 1Point System LLC

Senior Technical Recruiter
• Email: ranjeet@1pointsys.com • Fax: 803-832-7973 • www.1pointsys.com

https://www.linkedin.com/in/ranjeet-kumar-829a4525b/

If you are unable to reach me directly, please feel free to contact my supervisor at ashish.trivedi@1pointsys.com . They will be able to assist you with any inquiries or provide the support you need.


115 Stone Village Drive • Suite C • Fort Mill, SC • 29708

         An E-Verified company | An Equal Opportunity Employer