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Machine Learning Algorithms Jobs (NOW HIRING)

1 point system

Machine Learning Engineer

Fort Mill, SC · Remote

$48/hr

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 ...

New

CoVar

Machine Learning Engineer

Durham, NC · On-site

The Machine Learning Engineer will develop software and machine learning algorithms to address real-world customer issues and will have opportunities to present their work to high-level customers.

CoVar

Machine Learning Engineer

Durham, NC · On-site

In this role, you will help develop software and machine learning algorithms to address real-world customer challenges, working closely with data and presenting your findings to high-level customers.

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Manage machine learning algorithm lifecycle. * Coordinate data collection and annotation efforts. * Work with real-time data and content coming from various data sources. * Manage machine learning ...

NTENT

Machine Learning Engineer

Carlsbad, CA

Manage machine learning algorithm lifecycle. * Coordinate data collection and annotation efforts. * Work with real-time data and content coming from various data sources. * Manage machine learning ...

Skild AI

Machine Learning Engineer

San Mateo, CA

Position Overview We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and ...

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

Develop machine learning algorithms to drive personalized customer experiences and provide actionable business insights. * Apply expertise in data mining and machine learning techniques, including ...

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

Develop machine learning algorithms to drive personalized customer experiences and provide actionable business insights. * Apply expertise in data mining and machine learning techniques, including ...

EarnIn

Head of Machine Learning

Mountain View, CA · Hybrid

$477K - $583K/yr

Our mission is to pioneer success stories through the application of generative AI and state-of-the-art machine learning algorithms, thereby generating transformative business and societal impact.

Vadum Inc

Machine Learning Engineer

Raleigh, NC

Design, prototype, and implement machine learning algorithms using industry-standard frameworks. * Process, transform, visualize, and analyze data to guide the design and training processes of ...

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Machine Learning Algorithms information

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$25.5K

$42.6K

$88K

How much do machine learning algorithms jobs pay per year?

As of Jul 30, 2026, the average yearly pay for machine learning algorithms in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are machine learning algorithms?

Machine learning algorithms are computational methods that enable computers to learn patterns and make decisions or predictions from data without being explicitly programmed for each task. These algorithms can be classified into categories such as supervised learning, unsupervised learning, and reinforcement learning, each suited for different data and goals. Examples include decision trees, support vector machines, neural networks, and clustering algorithms. The choice of algorithm depends on the type of problem, the nature of the data, and the desired outcome.

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

To excel as a Machine Learning Algorithms Engineer, you need a solid background in mathematics, statistics, programming (especially Python or R), and a relevant degree in computer science or a related field. Familiarity with machine learning frameworks (like TensorFlow, PyTorch, or scikit-learn), data preprocessing tools, and cloud platforms is typically required, along with knowledge of version control systems. Strong analytical thinking, problem-solving abilities, and effective communication skills set top performers apart in this role. These skills and qualities are critical for designing robust models, collaborating with cross-functional teams, and translating complex data into actionable solutions.

What is the difference between Machine Learning Algorithms vs Data Scientists?

AspectMachine Learning AlgorithmsData Scientists
CredentialsKnowledge of algorithms, programming, statisticsAdvanced degrees in data science, statistics, or related fields
Work EnvironmentDeveloping, testing, and tuning algorithmsAnalyzing data, building models, interpreting results
Industry UsageEmbedded within data science workflows and toolsLeading data analysis projects, decision-making

While machine learning algorithms are the core tools used by data scientists, the role of a data scientist encompasses understanding, applying, and interpreting these algorithms within broader data analysis and business contexts. Machine learning algorithms are technical components, whereas data scientists integrate these tools to derive insights and inform strategies.

What are some common challenges faced when collaborating with cross-functional teams as a Machine Learning Algorithms specialist?

As a Machine Learning Algorithms specialist, collaborating with cross-functional teams such as data engineers, software developers, and product managers can present challenges like aligning on project goals, communicating complex technical concepts to non-experts, and integrating models into existing systems. It's important to establish clear communication channels, define shared objectives early, and actively participate in iterative feedback cycles. These practices help ensure that machine learning solutions are both technically sound and aligned with business needs.
More about Machine Learning Algorithms jobs
What cities are hiring for Machine Learning Algorithms jobs? Cities with the most Machine Learning Algorithms job openings:
What states have the most Machine Learning Algorithms jobs? States with the most job openings for Machine Learning Algorithms jobs include:
What job categories do people searching Machine Learning Algorithms jobs look for? The top searched job categories for Machine Learning Algorithms jobs are:
Machine Learning Algorithms jobs near you
Infographic showing various Machine Learning Algorithms job openings in the United States as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Engineer

1 point system

Fort Mill, SC • Remote

$48/hr

Contractor

Posted 3 days ago

New

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