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Temporary Machine Learning Trainer Jobs (NOW HIRING)

Hive

Machine Learning Manager

Seattle, WA

$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 ...

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 ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA

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 ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

Wehave 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 ...

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 ...

Motorola

Machine Learning Engineer

Los Angeles, CA · On-site

Contribute to the design of data pipelines and infrastructure for training, testing, and validating ... Stay current with the latest Machine Learning research for wireless and embedded systems. * Perform ...

ManTech International

Machine Learning Engineer

$128.80K - $214.50K/yr

Collaborate with cross-functional teams-including data scientists, engineers, and architects-to build machine learning models, training tools, and simulation environments that support business ...

Quanta Search

Machine Learning Engineer

Manhattan, NY · On-site

You'll be provided with the tools, resources and training required to satisfy that curiosity and ... Machine Learning Engineers build production grade machine learning algorithms that operate in real ...

Anytime AI

Be Seen First

Machine Learning Engineer

OR · Remote

$100K - $200K/yr

Position Overview As a Machine Learning Engineer, you will be instrumental in crafting and refining ... Experience in training Large Language Models. * Web development experience or willingness to ...

Hadrian

Machine Learning Engineer

Los Angeles, CA · On-site

... training, inference, labeling, and evaluation • Judiciously combine open-source solutions and ... Machine Learning, including ownership of projects throughout the entire ML Lifecycle • ...

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All 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 May 30, 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 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 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 Temporary Machine Learning Trainers?

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 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.

More about Temporary Machine Learning Trainer jobs
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:
What job categories do people searching Temporary Machine Learning Trainer jobs look for? The top searched job categories for Temporary Machine Learning Trainer jobs are:
Temporary Machine Learning Trainer jobs near you
Infographic showing various Temporary Machine Learning Trainer job openings in the United States as of May 2026, with employment types broken down into 19% Full Time, and 81% Part Time. Highlights an 100% Physical job distribution, with an average salary of $87,325 per year, or $42 per hour.

Machine Learning Engineer

Purple Drive Technologies

Newark, NJ • On-site

Full-time

Retirement

Posted 29 days ago

Job description

Overview:
Role: Machine Learning Engineer
As a Machine Learning Engineer, you will play a critical role in designing, developing, and deploying advanced machine learning solutions that drive innovation and create measurable business value. You will collaborate closely with business stakeholders, data scientists, and cross-functional engineering teams to transform ideas into scalable, production-ready systems. This role requires both strong technical expertise and leadership ability to guide a small team and deliver impactful solutions.
Key Responsibilities
  • Lead and deliver machine learning projects from inception through production, building strong relationships with business partners and cross-functional teams.
  • Collaborate with business leaders and subject matter experts to define success criteria and optimize products, features, and models.
  • Partner with data scientists to design, implement, and train machine learning models.
  • Work with infrastructure teams to enhance architecture, scalability, stability, and performance of ML platforms.
  • Design and optimize data pipelines to support high-performance ML model training and inference.
  • Extend and customize machine learning libraries and frameworks for project-specific needs.
  • Define and implement model monitoring, governance, and operationalization processes for ML solutions.
  • Establish objectives and own the technical roadmap for ML platforms, ensuring delivery of results.
  • Define and promote standards of engineering and operational excellence for ML systems.
  • Apply architectural best practices in the delivery of data science and AI solutions.
Required Skills & Experience
  • Strong background in software engineering with proven experience as a Machine Learning Engineer.
  • Bachelor's degree in Computer Science, Computer Engineering, or related field; Master's preferred.
  • Advanced proficiency in Python, Java, and Scala with solid CS fundamentals (algorithms, data structures, multithreading).
  • Hands-on experience with Generative AI, LangChain, and RAG-based techniques.
  • Expertise in ML/DL libraries such as XGBoost, Scikit-learn, TensorFlow, and PyTorch.
  • Experience building and deploying ML solutions on public clouds (AWS, GCP).
  • Familiarity with ML platforms such as SageMaker, H2O, and DataRobot.
  • Strong knowledge of the ML lifecycle, including containerization, batch vs. real-time inference, and application security.
  • Proven track record in developing and deploying production-grade ML applications with cloud-based automation pipelines.
  • Experience working in Agile/Scrum environments with multiple stakeholders.
  • Excellent communication, collaboration, and problem-solving skills with thought leadership and innovative thinking.
Nice-to-Have
  • Experience with search platforms (e.g., Solr, Elasticsearch).
  • Hands-on experience building recommender systems.
  • Exposure to graph databases (e.g., Neo4j).
  • Familiarity with CI/CD tools (e.g., Jenkins).
  • Domain knowledge in Financial Services, Insurance, or 401K.
  • AWS Solutions Architect certification.

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