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Apprentice Machine Learning Testing Jobs in New York, NY

Chefman

Machine Learning Engineer

Mahwah, NJ

As a Machine Learning Engineer, you will play a critical role in shaping the future of cooking ... Establish testing methodologies and performance metrics to validate models across real-world usage ...

Kitware

Machine Learning Engineer

New York, NY · On-site

$85K - $125K/yr

Strong background in both classical and modern (deep learning) machine learning, including model selection, architecting, training, validation, testing, and deployment * Machine learning experience ...

WITHIN

Machine Learning Engineer

New York, NY · Hybrid

$90K - $254K/yr

About the Role : We are in search of an exceptional Machine Learning Engineer to join our ... Causal inference / experimentation- geo experiments (matched markets), A/B testing at scale.

Chasm Select

Senior Machine Learning Engineer

Manhattan, NY · On-site

$150K - $180K/yr

Description ChasmTeam Senior Machine Learning Engineer at JudiHealth Location: Remote (For Non ... Participate in code reviews, testing, and quality assurance processes. * Troubleshoot and resolve ...

NYULMC

Machine Learning Engineer

New York, NY · On-site

... development, evaluation, testing, deployment, refactoring and technical support Minimum ... of AI, machine learning, and deep learning 5. Experience with AI platforms like PyTorch or ...

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Apprentice Machine Learning Testing information

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

As of Jun 28, 2026, the average hourly pay for apprentice machine learning testing in New York, NY is $21.18, according to ZipRecruiter salary data. Most workers in this role earn between $17.88 and $23.12 per hour, depending on experience, location, and employer.

What kinds of projects or tasks can I expect to work on as an Apprentice Machine Learning Testing?

As an Apprentice Machine Learning Testing, you’ll typically assist in evaluating machine learning models by designing and running tests, analyzing model outputs, and helping identify issues like bias or overfitting. You may work closely with data scientists and software engineers to validate model performance and ensure results align with project objectives. Your daily tasks might include preparing test datasets, executing automated testing scripts, and documenting findings to help improve model reliability. This role often serves as a valuable introduction to practical machine learning workflows and quality assurance processes in technical teams.

What are the key skills and qualifications needed to thrive as an Apprentice Machine Learning Testing, and why are they important?

To thrive as an Apprentice in Machine Learning Testing, a foundational understanding of statistics, programming (especially Python), and basic machine learning concepts is essential, often supported by a degree or coursework in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, Jupyter Notebooks, and version control systems is typically required. Strong analytical thinking, attention to detail, and effective communication skills help apprentices collaborate and identify testing issues efficiently. These skills ensure accurate model validation, effective troubleshooting, and contribute to the robust deployment of machine learning solutions.

What does an Apprentice Machine Learning Testing do?

An Apprentice Machine Learning Testing professional assists in evaluating and validating machine learning models to ensure they perform as expected. They typically work under the guidance of experienced data scientists or engineers, running tests, analyzing results, and helping to identify issues such as bias or inaccuracies in algorithms. Their responsibilities may also include developing test cases, writing reports, and learning about data preprocessing and evaluation metrics. This role is ideal for those who are new to the field and want to build foundational skills in machine learning quality assurance.

What is the difference between Apprentice Machine Learning Testing vs Machine Learning Engineer?

AspectApprentice Machine Learning TestingMachine Learning Engineer
Required CredentialsBasic understanding of ML concepts, often pursuing relevant certifications or degreesAdvanced degrees (BSc, MSc, PhD) in CS or related fields, with extensive experience
Work EnvironmentEntry-level, supervised testing environments, often in training programsFull-time, independent development and deployment of ML models in production
Employer & Industry UsageInternships, training programs, entry-level roles in tech companiesEstablished tech firms, startups, research institutions

Apprentice Machine Learning Testing roles focus on learning and assisting with testing ML models under supervision, while Machine Learning Engineers design, build, and deploy ML systems independently. The apprentice position is ideal for gaining foundational skills, whereas the engineer role requires advanced expertise and experience.

What are the most commonly searched types of Machine Learning Testing jobs in New York, NY? The most popular types of Machine Learning Testing jobs in New York, NY are:
What are popular job titles related to Apprentice Machine Learning Testing jobs in New York, NY? For Apprentice Machine Learning Testing jobs in New York, NY, the most frequently searched job titles are:
What job categories do people searching Apprentice Machine Learning Testing jobs in New York, NY look for? The top searched job categories for Apprentice Machine Learning Testing jobs in New York, NY are:
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Apprentice Machine Learning Testing jobs near you
Machine Learning Engineer

Machine Learning Engineer

Chefman

Mahwah, NJ

Other

Posted 24 days ago

Key responsibilities

  • Design, train, and deploy machine learning and computer vision models that power autonomous cooking experiences within CHEF iQ products.

  • Build and manage datasets, including data collection, labeling, preparation, augmentation, and validation.

  • Collaborate with firmware, software, hardware, and product teams to integrate machine learning capabilities into consumer products.

Job description

About CHEF iQ

In 2020, we launched CHEF iQ, an ecosystem of connected kitchen appliances designed to transform how people cook and connect through food. Our mission is to make great cooking effortless through intelligent technology, guided experiences, and seamless integration between hardware, software, and AI.

As a Machine Learning Engineer, you will play a critical role in shaping the future of cooking. Working on a small, high-impact team, you will have significant ownership over the strategy, research, development, and deployment of AI capabilities that power next-generation kitchen products. From computer vision models that understand what is happening inside an oven to embedded AI systems that make real-time cooking decisions, you will help define how machine learning is applied within consumer appliances.

This is an opportunity to work at the intersection of machine learning, embedded systems, computer vision, and smart consumer technology, bringing cutting-edge AI from research into products used by millions of home cooks.

Role and Responsibilities

Design, train, and deploy machine learning and computer vision models that power autonomous cooking experiences within CHEF iQ products.
Develop image classification, object detection, and state-recognition models that identify food types, cooking progress, doneness levels, and other key inputs used to guide cooking decisions.
Build and manage datasets, including data collection, labeling, preparation, augmentation, and validation.
Own the full machine learning lifecycle, from data preparation and model training through deployment, monitoring, and continuous improvement.
Research, evaluate, and apply emerging machine learning techniques, including computer vision, generative AI, large language models (LLMs), vision-language models (VLMs), multimodal AI, and academic research, to improve product performance and customer experiences.
Deploy and optimize models for cloud and edge devices, balancing accuracy, latency, memory usage, power consumption, and overall system performance.
Collaborate with firmware, software, hardware, and product teams to integrate machine learning capabilities into consumer products.
Develop systems that combine vision, sensor, and contextual data to enable intelligent recommendations and autonomous next-step actions.
Design and develop AI-driven systems that combine perception, reasoning, and decision-making capabilities to enable intelligent and autonomous cooking experiences.
Establish testing methodologies and performance metrics to validate models across real-world usage scenarios.
Document model architectures, experiments, and deployment approaches.

Qualifications

Experience developing and deploying machine learning models in production environments.
Strong experience with computer vision, image classification, object detection, deep learning, or related machine learning applications.
Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or similar technologies.
Experience building and managing datasets used for machine learning model development.
Experience deploying or optimizing models for embedded systems, edge devices, or resource-constrained environments.
Experience working with public cloud platforms such as AWS, Google Cloud Platform, or Microsoft Azure, including their machine learning and AI services.
Experience with multimodal foundation models, vision-language models (VLMs), or other AI systems that combine vision, language, and contextual understanding.
Experience with MLOps practices including model lifecycle management, experiment tracking, model monitoring, and CI/CD pipelines for machine learning systems.
Understanding of model optimization techniques such as quantization, pruning, and inference acceleration.
Ability to independently evaluate new technologies, research, and model architectures.
Strong analytical, problem-solving, and debugging skills.
Excellent communication and cross-functional collaboration skills.

Preferred Qualifications

Experience with embedded Linux, ARM-based platforms, or edge AI hardware.
Experience with TensorFlow Lite, ONNX Runtime, OpenVINO, TensorRT, or similar deployment frameworks.
Experience with connected consumer products, IoT devices, robotics, or embedded vision systems.
Experience with large language models (LLMs), small language models (SLMs), vision-language models (VLMs), generative AI, recommendation systems, agentic AI systems, or AI-powered user experiences.
Experience with retrieval-augmented generation (RAG), vector databases, embeddings, semantic search, or knowledge retrieval systems.
Experience designing AI agents capable of monitoring, planning, reasoning, and decision-making using vision, sensor, and contextual data.
Experience with AWS machine learning and AI services preferred.

This position requires U.S. work authorization. We are not able to provide visa sponsorship at this time.

Apply