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Temporary Machine Learning Testing Jobs (NOW HIRING)

ExtendMyTeam

Machine Learning Engineer

Austin, TX

Support experimentation, evaluation, testing, and continuous improvement of AI systems * Stay current with emerging AI, LLM, and machine learning technologies Required Experience / Ideal Background ...

Avride

Machine Learning Engineer

Austin, TX · On-site

Develop efficient workflows for training, validation, and testing, incorporating distributed ... Strong understanding of fundamental machine learning algorithms and neural network techniques.

CoVar

Machine Learning Engineer

Durham, NC

CI/CD pipelines and regression testing. * AI/ML expertise: Machine learning fundamentals; Deep knowledge of state-of-the-art in any of the following: computer vision (preferred), natural language ...

Avride

Machine Learning Engineer

Austin, TX · On-site

They are seeking an experienced Machine Learning Engineer to design, implement, and optimize ... Develop efficient workflows for training, validation, and testing, incorporating distributed ...

Micron

Machine Learning Engineer

Boise, ID · On-site

Position requires experience in: 1. Building and executing end-to-end ML systems automating training, testing, and deploying Machine Learning models in cloud platforms 2. Machine learning frameworks ...

Hive

Machine Learning Manager

Seattle, WA

$180K - $250K/yr

Machine Learning Manager In order to execute our vision, we're constantly growing our machine ... testing, and deployment in production. * Drive delivery for our product milestones, continually ...

Machina Labs

Machine Learning Engineer

Chatsworth, 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 ...

Spotify

Machine Learning Engineer

... testing, evaluation, etc., both inside the team as well as throughout the organization. Qualifications : Required : • Strong background in machine learning • Expertise in statistics and ...

Sarian, Inc.

Python Developer

Pittsburgh, PA · On-site

$48.75 - $67.25/hr

Data Analysis/Machine Learning. * Testing/Debugging/Security. * Writing, testing, and debugging Python code for various applications. * Developing server-side logic, back-end components, and APIs.

Spotify

Machine Learning Engineer

... testing, evaluation, etc., both inside the team as well as throughout the organization. Qualifications : Required : • You have a strong background in machine learning, enjoy applying theory to ...

Micron Technology

Machine Learning Engineer

Boise, ID · On-site

Position requires experience in: 1. Building and executing end-to-end ML systems automating training, testing, and deploying Machine Learning models in cloud platforms 2. Machine learning frameworks ...

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Temporary Machine Learning Testing information

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How much do temporary machine learning testing jobs pay per hour?

As of Jun 3, 2026, the average hourly pay for temporary machine learning testing in the United States is $22.82, according to ZipRecruiter salary data. Most workers in this role earn between $19.71 and $25.48 per hour, depending on experience, location, and employer.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and test AI models, and their role involves understanding complex algorithms and data handling. While AI automation tools can assist with certain tasks, MLEs are essential for creating, fine-tuning, and maintaining AI systems, making complete replacement unlikely in the near term. Skills in programming, data analysis, and model evaluation remain critical for MLEs working alongside AI technologies.

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

AspectTemporary Machine Learning TestingData Scientist
CredentialsTypically requires knowledge of machine learning tools, programming, and basic statisticsRequires advanced degrees (e.g., Master’s or PhD) in data science, statistics, or related fields
Work EnvironmentProject-based, often temporary roles focused on testing models and algorithmsLong-term, strategic roles involving data analysis, model development, and business insights
Industry UsageCommon in tech, finance, and research sectors for specific testing tasksWidely used across industries for data-driven decision making

Temporary Machine Learning Testing roles focus on evaluating and validating machine learning models in short-term projects, while Data Scientists develop, implement, and interpret complex data models for ongoing business strategies. Both roles require technical skills, but Data Scientists typically have higher educational credentials and broader responsibilities.

More about Temporary Machine Learning Testing jobs
What cities are hiring for Temporary Machine Learning Testing jobs? Cities with the most Temporary Machine Learning Testing job openings:
What are the most commonly searched types of Machine Learning Testing jobs? The most popular types of Machine Learning Testing jobs are:
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What job categories do people searching Temporary Machine Learning Testing jobs look for? The top searched job categories for Temporary Machine Learning Testing jobs are:
Temporary Machine Learning Testing jobs near you
Infographic showing various Temporary Machine Learning Testing job openings in the United States as of May 2026, with employment types broken down into 68% Full Time, 16% Part Time, 3% Temporary, 10% Contract, and 3% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $47,468 per year, or $22.8 per hour.

Machine Learning Engineer

RZR Global Inc.

San Francisco, CA • On-site

Other

Posted 14 days ago

Job description

Who are we?

RZR Global is an AI-driven company specializing in mobile advertising solutions designed to fuel revenue growth. We leverage AI to discover audiences in a privacy-first environment through trillions of contextual bidding signals and proprietary behavioral models. Our audience engagement platform includes creative strategy and execution. We handle 5 million mobile ad requests per second from over 10 billion devices, driving performance for both publishers and brands. We are headquartered in San Francisco, CA, with a global presence across the United States, EMEA, and APAC.
The role?

We are seeking a motivated and detail-oriented Machine Learning Engineer to join our team. As an ML Engineer, you will be involved in designing and implementing machine learning models and data pipelines to enhance our programmatic demand-side platform (DSP). You will work closely with Senior MLE and other team members to drive impactful machine learning projects and contribute to innovative solutions.

What will you do?
  • Support the development of machine learning models to address challenges in programmatic advertising, such as predicting user responses, forecasting bid landscapes, and detecting fraud.
  • Collaborate with senior data scientists and cross-functional teams (product, engineering, and analytics) to integrate models into production workflows.
  • Analyze the impact of integrating new data sources and features into our models.
  • Build and maintain data pipelines to process and prepare large datasets for model training and evaluation.
  • Contribute ideas and assist in testing new tools, methodologies, and technologies to improve our machine learning capabilities.
  • Document experiments, assumptions, and outcomes; maintain reproducibility
What are we looking for?
  • Bachelor's degree in Mathematics, Physics, Computer Science, or a related technical field.
  • At least 2 years of professional experience in machine learning, statistical analysis, and data analysis.
  • Experience with machine learning techniques such as regression, classification, and clustering.
  • Proficiency in Python and SQL and familiarity with big data tools (e.g., Spark) and ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
  • Strong grasp of probability, statistics, and data analysis principles.
  • Ability to work effectively in a team environment, with good communication skills to explain complex concepts to diverse stakeholders.
Nice-to-Have
  • Familiarity with system programming languages including C++ and Rust is a plus.
  • Exposure to online inference systems, gRPC/REST model endpoints, or streaming features (Kafka/Flink)
  • Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization.

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