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Entry Level Google Machine Learning Engineer Jobs in Houston, TX

YO AI Labs

AI/ML Engineer - Remote

Houston, TX · Remote

$200 - $350/hr

... machine learning applications. Key Responsibilities * Design, implement, and optimize AI/ML ... Deploy AI solutions across secure cloud environments such as AWS GovCloud, Google GovCloud, Azure ...

Fastenal Company

Entry-Level Machinist

Houston, TX · On-site

$14 - $17/hr

Entry-Level Machinist 15009 Lee Rd, Houston, TX 77032 TX280 Manufacturing Facility Full-time Shift ... Learning machine operation and capabilities of multiple machines o Running multiple pieces of ...

Fastenal Company

Entry-Level Machinist

Houston, TX · On-site

$15.50 - $18.75/hr

Entry-Level Machinist 15009 Lee Rd, Houston, TX 77032 TX280 Manufacturing Facility Full-time Shift ... Learning machine operation and capabilities of multiple machines o Running multiple pieces of ...

Fastenal Company

Entry-Level Machinist

Houston, TX · On-site

$14 - $17/hr

Entry-Level Machinist 15009 Lee Rd, Houston, TX 77032 TX280 Manufacturing Facility Full-time Shift ... Learning machine operation and capabilities of multiple machines o Running multiple pieces of ...

Pwc

AI & GenAI Data Scientist - Manager

Houston, TX · On-site

$99K - $232K/yr

... engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...

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All JobsGoogle Machine Learning Engineer JobsEntry Level Google Machine Learning Engineer JobsEntry Level Google Machine Learning Engineer Jobs in Houston, TX

Entry Level Google Machine Learning Engineer information

See Houston, TX salary details

$28.6K

$66.2K

$112.7K

How much do entry level google machine learning engineer jobs pay per year?

As of Sep 7, 2026, the average yearly pay for entry level google machine learning engineer in Houston, TX is $66,239.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,200.00 and $75,000.00 per year, depending on experience, location, and employer.

What is an entry level Google machine learning engineer?

Entry Level Google Machine Learning Engineers are professionals who have recently started their careers in machine learning and work at Google. They typically assist in designing, developing, and deploying machine learning models to solve real-world problems. Their responsibilities may include data preprocessing, feature engineering, model training, evaluation, and collaborating with senior engineers and researchers. These roles often require a strong foundation in programming, mathematics, and statistics, as well as familiarity with machine learning frameworks such as TensorFlow or PyTorch. Entry Level Machine Learning Engineers at Google usually work on supervised projects and are mentored by more experienced team members.

What are the typical projects and responsibilities for an entry level Google machine learning engineer?

As an Entry Level Machine Learning Engineer at Google, you can expect to work on a variety of projects ranging from building and optimizing machine learning models to supporting data preprocessing and feature engineering tasks. You will often collaborate with senior engineers, data scientists, and product teams to implement solutions that address real-world problems at scale. Your daily responsibilities may include coding in Python or TensorFlow, participating in code reviews, and troubleshooting model performance. This role offers hands-on experience with industry-leading tools and the opportunity to learn from experienced colleagues, making it a great foundation for career growth in AI and machine learning.

What are the key skills and qualifications needed to thrive as an entry level Google machine learning engineer, and why are they important?

To thrive as an Entry Level Google Machine Learning Engineer, you need a solid foundation in computer science, statistics, and mathematics, typically with at least a bachelor's degree in a related field. Familiarity with programming languages like Python or Java, experience using TensorFlow or PyTorch, and understanding of cloud platforms such as Google Cloud are essential technical requirements. Strong problem-solving skills, teamwork, and effective communication help you collaborate and convey complex concepts clearly. These skills and qualities are crucial for building scalable machine learning solutions and contributing effectively in a dynamic, innovative environment.

What is the difference between Entry Level Google Machine Learning Engineer vs Entry Level Data Scientist?

AspectEntry Level Google Machine Learning EngineerEntry Level Data Scientist
Required CredentialsBachelor's in CS, Math, or related; knowledge of ML frameworksBachelor's in CS, Stats, or related; strong analytical skills
Work EnvironmentDeveloping ML models, deploying algorithms, coding in Python/JavaData analysis, statistical modeling, data visualization
Employer & Industry UsageTech companies, especially Google, focusing on AI/ML productsVarious industries including tech, finance, healthcare

Entry Level Google Machine Learning Engineers focus on developing and deploying machine learning models, often requiring coding and understanding of ML frameworks. Entry Level Data Scientists analyze data, build statistical models, and create visualizations. While both roles require similar educational backgrounds, their daily tasks and focus areas differ, with ML Engineers more involved in algorithm implementation and Data Scientists in data analysis and insights.

What are popular job titles related to Entry Level Google Machine Learning Engineer jobs in Houston, TX?

For Entry Level Google Machine Learning Engineer jobs in Houston, TX, the most frequently searched job titles are:

What job categories do people searching Entry Level Google Machine Learning Engineer jobs in Houston, TX look for?

The top searched job categories for Entry Level Google Machine Learning Engineer jobs in Houston, TX are:

Entry Level Google Machine Learning Engineer jobs near you

Senior Algorithm Engineer, Deep Learning & Vision

Socket.dev

Houston, TX • On-site

$150 - $210/hr

Other

Re-posted 2 days ago

Job description

Company Introduction

At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.


Key Responsibilities

  • Explore and propose new ideas using your knowledge and experience in deep learning, neural networks and large foundation models in autonomous driving including: end-to-end object detection, tracking and prediction, end-to-end planning and control, and end-to-end autonomous driving system, end-to-end online mapping. SLAM in localization and etc..

  • Work on the entire life cycle of machine learning projects from data analysis, model experimentations to performance metrics verifications, and understand the entire workflow in great detail.

  • Be exposed to many cross-team projects and collaborate with the product, simulation and other sibling autonomous driving algorithm teams to extend machine learning technology to all components.


Qualifications
Required:

  • Have an advanced degree (Ph.D or Master’s) in related fields of study: computer science, computer engineering, robotics, mathematics, physics, and etc.

  • Have in-depth knowledge and extensive experience in machine learning, and/or computer vision, modern transformer architecture, and employ SOTA techniques of machine learning.

  • Be familiar with PyTorch, TensorFlow and other machine learning platforms and tools.

  • Have strong motivation to work independently in a fast paced environment while collaborating with other teams on more complex and larger projects.


Preferred:

  • Have a proven track record of research publications in top conferences and/or journals as the first author.

  • Have knowledge and experience of generative models, model distillation, or model inference acceleration (e.g. TensorRT) techniques.

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