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Machine Learning Testing Jobs (NOW HIRING)

We are looking for a Machine Learning Engineer to design, build, and deploy machine learning ... Solid software engineering fundamentals (architecture, Git workflows, testing, code review)

Python Developer

Pittsburgh, PA ยท 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.

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

The role involves developing and optimizing machine learning models, managing large-scale datasets ... Develop efficient workflows for training, validation, and testing, incorporating distributed ...

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 ...

REMOTE Machine Learning Engineer This project-based consulting role invites an experienced Machine ... Hands-on experience with A/B testing, experimentation design, and causal inference approaches.

AI/Machine Learning Engineer This project-based consulting role invites an experienced Machine ... Hands-on experience with A/B testing, experimentation design, and causal inference approaches.

A/B Testing and Experimentation : Design and conduct A/B tests to assess the performance of different machine learning models. This includes setting up the test environment, monitoring performance ...

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How much do machine learning testing jobs pay per hour?

As of Sep 12, 2026, the average hourly pay for 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.

What is a machine learning testing?

A Machine Learning Testing job involves evaluating and validating machine learning models to ensure they function correctly, efficiently, and ethically. This includes testing for accuracy, reliability, bias, and performance under different conditions. Professionals in this role employ techniques such as unit testing, integration testing, data validation, and model performance monitoring. They also work closely with data scientists and engineers to debug issues and improve model robustness. The goal is to ensure that machine learning systems perform as expected and meet business or regulatory requirements.

What are the typical challenges faced by professionals in machine learning testing roles?

Professionals in Machine Learning Testing often encounter challenges such as dealing with non-deterministic model outputs, insufficient or imbalanced datasets, and unclear or evolving testing criteria. They may need to work closely with data scientists and engineers to develop robust test cases and validation methods tailored for dynamic machine learning systems. Staying updated on advancements in testing methodologies and tools is also important, as the field evolves rapidly. Successfully overcoming these challenges leads to higher quality models and more reliable AI solutions for end users.

What are the key skills and qualifications needed to thrive in machine learning testing, and why are they important?

To excel in Machine Learning Testing, you need a solid understanding of machine learning concepts, data analysis, and programming skills in languages like Python, as well as a background in quality assurance or software testing. Familiarity with frameworks such as TensorFlow, PyTorch, automated testing tools, and relevant certifications like ISTQB are highly beneficial. Strong attention to detail, analytical thinking, and effective communication skills help testers identify issues and collaborate with data scientists and developers. These competencies are essential to ensure the reliability, fairness, and accuracy of machine learning models deployed in production environments.

How do I become a machine learning testing?

To become a machine learning testing professional, you typically need a strong background in computer science, programming skills in languages like Python or Java, and knowledge of machine learning frameworks such as TensorFlow or PyTorch. Gaining experience with data analysis, model evaluation, and testing methodologies, along with relevant certifications or training, can improve your qualifications for this role.
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Infographic showing various Machine Learning Testing job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $47,468 per year, or $22.8 per hour.

Machine Learning

Manhattan, NY โ€ข On-site

Cantor Fitzgerald Securities
Finance and Insuranceย โ€ขย 10K+ employees

Full-time

Posted 10 days ago


Job description


Join Cantor Fitzgerald Technology Markets LLC as a Machine Learning Engineer focused on building AI-driven solutions for a high-volume financial services business. You will work closely with product, engineering, and business teams to create, test, and operationalize large language model (LLM) applications, ensuring they meet performance, reliability, and responsible-AI standards.
Responsibilities
  • Design and implement LLM-driven features in production systems.
  • Build and maintain data pipelines for both structured and unstructured data.
  • Write clean, testable Python code and maintain reusable libraries.
  • Develop prompts, tool-calling workflows, and retrieval pipelines.
  • Create evaluation suites, define success metrics, and analyze failures.
  • Diagnose and mitigate hallucination, latency, and cost issues.
  • Collaborate with product, engineering, and business stakeholders.
  • Implement monitoring, logging, and alerting for AI services.
  • Contribute to responsible-AI guardrails and human-in-the-loop processes.
  • Document designs, experiments, and findings for internal knowledge sharing.

Qualifications
  • Bachelor's degree in computer science, machine learning, mathematics, physics, statistics, econometrics, or equivalent practical experience.
  • Experience contributing to production or production-like software through work, internships, research, open source, or substantial personal projects.
  • Strong programming ability in Python with clear, tested, and maintainable code.
  • Experience with web services, data integrations, testing, logging, and basic monitoring across diverse data types.
  • Hands-on experience building with LLM tools or frameworks (prompting, structured outputs, tool-calling, retrieval, multi-step workflows) and awareness of common failure modes.
  • Experience evaluating LLM-powered applications: building test sets, reviewing failures, defining metrics, and iterating on prompts or retrieval.
  • Solid grounding in machine learning, statistics, and experimental design with ability to interpret technical papers and documentation.
  • Strong communication skills and comfort working with product, engineering, and business partners.
  • Interest in applying AI responsibly in financial services, including privacy, security, human review, and appropriate automation.
  • Familiarity with cloud deployment, containers, and modern release pipelines.

$140,000 - $160,000